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Detection and classification of vocal productions in large scale audio recordings
Published 14 Feb 2023 in cs.SD, cs.LG, eess.AS, and stat.AP | (2302.07640v2)
Abstract: We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings and classify these vocal productions. The pipeline is based on a deep neural network and adresses both issues simultaneously. Though a series of computationel steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network without requiring a large sample of labeled data and important computing resources. Our end-to-end methodology can handle noisy recordings made under different recording conditions. We test it on two different natural audio data sets, one from a group of Guinea baboons recorded from a primate research center and one from human babies recorded at home. The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94.58% and 99.76%. It is then used to process 443 and 174 hours of natural continuous recordings and it creates two new databases of 38.8 and 35.2 hours, respectively. We discuss the strengths and limitations of this approach that can be applied to any massive audio recording.
- Xia, X., Togneri, R., Sohel, F., Huang, D.: Random forest classification based acoustic event detection utilizing contextual-information and bottleneck features. Pattern Recognition 81, 1–13 (2018) https://doi.org/10.1016/j.patcog.2018.03.025 Nguyen et al. [2018] Nguyen, T.T.T., Nguyen, T.T., Liew, A.W.-C., Wang, S.-L.: Variational inference based bayes online classifiers with concept drift adaptation. Pattern Recognition 81, 280–293 (2018) https://doi.org/10.1016/j.patcog.2018.04.007 Strisciuglio et al. [2019] Strisciuglio, N., Vento, M., Petkov, N.: Learning representations of sound using trainable COPE feature extractors. Pattern Recognition 92, 25–36 (2019) https://doi.org/10.1016/j.patcog.2019.03.016 LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Nguyen, T.T.T., Nguyen, T.T., Liew, A.W.-C., Wang, S.-L.: Variational inference based bayes online classifiers with concept drift adaptation. Pattern Recognition 81, 280–293 (2018) https://doi.org/10.1016/j.patcog.2018.04.007 Strisciuglio et al. [2019] Strisciuglio, N., Vento, M., Petkov, N.: Learning representations of sound using trainable COPE feature extractors. Pattern Recognition 92, 25–36 (2019) https://doi.org/10.1016/j.patcog.2019.03.016 LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Strisciuglio, N., Vento, M., Petkov, N.: Learning representations of sound using trainable COPE feature extractors. Pattern Recognition 92, 25–36 (2019) https://doi.org/10.1016/j.patcog.2019.03.016 LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Nguyen, T.T.T., Nguyen, T.T., Liew, A.W.-C., Wang, S.-L.: Variational inference based bayes online classifiers with concept drift adaptation. Pattern Recognition 81, 280–293 (2018) https://doi.org/10.1016/j.patcog.2018.04.007 Strisciuglio et al. [2019] Strisciuglio, N., Vento, M., Petkov, N.: Learning representations of sound using trainable COPE feature extractors. Pattern Recognition 92, 25–36 (2019) https://doi.org/10.1016/j.patcog.2019.03.016 LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Strisciuglio, N., Vento, M., Petkov, N.: Learning representations of sound using trainable COPE feature extractors. Pattern Recognition 92, 25–36 (2019) https://doi.org/10.1016/j.patcog.2019.03.016 LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Strisciuglio, N., Vento, M., Petkov, N.: Learning representations of sound using trainable COPE feature extractors. Pattern Recognition 92, 25–36 (2019) https://doi.org/10.1016/j.patcog.2019.03.016 LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) https://doi.org/10.1038/nature14539 Stowell et al. [2018] Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Stowell, D., Stylianou, Y., Wood, M., Pamuła, H., Glotin, H.: Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. arXiv:1807.05812 [cs, eess] (2018) arXiv:1807.05812 [cs, eess] Bergler et al. [2019] Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E., Hofer, H., Maier, A.: ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports 9(1), 10997 (2019) https://doi.org/10.1038/s41598-019-47335-w Oikarinen et al. [2019] Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Oikarinen, T., Srinivasan, K., Meisner, O., Hyman, J.B., Parmar, S., Fanucci-Kiss, A., Desimone, R., Landman, R., Feng, G.: Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2), 654–662 (2019) https://doi.org/10.1121/1.5087827 Stowell [2022] Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Stowell, D.: Computational bioacoustics with deep learning: A review and roadmap. PeerJ 10, 13152 (2022) https://doi.org/10.7717/peerj.13152 Pijanowski et al. [2011] Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L.: What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26(9), 1213–1232 (2011) https://doi.org/10.1007/s10980-011-9600-8 Choi et al. [2017] Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Choi, K., Fazekas, G., Sandler, M., Cho, K.: Transfer learning for music classification and regression tasks. In: International Society for Music Information Retrieval Conference (2017) Hershey et al. [2017] Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., Wilson, K.: Cnn architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017) Palanisamy et al. [2020] Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Palanisamy, K., Singhania, D., Yao, A.: Rethinking CNN Models for Audio Classification. arXiv:2007.11154 [cs, eess] (2020) arXiv:2007.11154 [cs, eess] Aytar et al. [2016] Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: Learning Sound Representations from Unlabeled Video. arXiv:1610.09001 [cs] (2016) arXiv:1610.09001 [cs] [15] Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Plakal, M., Ellis, D.: TensorFlow Hub. https://tfhub.dev/google/yamnet/1 Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 [cs] (2017) arXiv:1704.04861 [cs] Gemmeke et al. [2017] Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261 Tena et al. [2022] Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Tena, A., Clarià , F., Solsona, F.: Automated detection of COVID-19 cough. Biomedical Signal Processing and Control 71, 103175 (2022) Patil and Wani [2023] Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Patil, S., Wani, K.: Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network. Materials Today: Proceedings 72, 1322–1327 (2023) McFee et al. [2015] McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- McFee, B., Humphrey, E.J., Bello, J.P.: A software framework for musical data augmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, 248–254 (2015) He et al. [2015] He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs] (2015) arXiv:1502.01852 [cs] Kumar [2017] Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Kumar, S.K.: On weight initialization in deep neural networks. arXiv:1704.08863 [cs] (2017) arXiv:1704.08863 [cs] Ioffe and Szegedy [2015] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015) arXiv:1502.03167 [cs] Srivastava et al. [2014] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014) Dozat [2016] Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Dozat, T.: Incorporating Nesterov Momentum into Adam. In: ICLR Workshop, p. 4 (2016) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs] (2017) arXiv:1412.6980 [cs] Brochu et al. [2010] Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Brochu, E., Cora, V.M., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs] (2010) arXiv:1012.2599 [cs] Snoek et al. [2012] Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ??? (2012) Shahriari et al. [2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 Boë et al. [2017] Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Boë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T.R., Becker, Y., Rey, A., Fagot, J.: Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors. PLOS ONE 12(1), 0169321 (2017) https://doi.org/10.1371/journal.pone.0169321 Cychosz et al. [2019] Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Cychosz, M., Seidl, A., Bergelson, E., Casillas, M., Baudet, G., Warlaumont, A.S., Scaff, C., Yankowitz, L., Cristia, A.: BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages (2019) https://doi.org/10.17605/OSF.IO/RZ4TX Abadi et al. [2015] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/ Prat [2019] Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402 Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
- Prat, Y.: Animals Have No Language, and Humans Are Animals Too. Perspectives on Psychological Science 14(5), 885–893 (2019) https://doi.org/10.1177/1745691619858402
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