2000 character limit reached
Stream-based Active Learning for Anomalous Sound Detection in Machine Condition Monitoring (2408.05493v1)
Published 10 Aug 2024 in cs.SD and eess.AS
Abstract: This paper introduces an active learning (AL) framework for anomalous sound detection (ASD) in machine condition monitoring system. Typically, ASD models are trained solely on normal samples due to the scarcity of anomalous data, leading to decreased accuracy for unseen samples during inference. AL is a promising solution to solve this problem by enabling the model to learn new concepts more effectively with fewer labeled examples, thus reducing manual annotation efforts. However, its effectiveness in ASD remains unexplored. To minimize update costs and time, our proposed method focuses on updating the scoring backend of ASD system without retraining the neural network model. Experimental results on the DCASE 2023 Challenge Task 2 dataset confirm that our AL framework significantly improves ASD performance even with low labeling budgets. Moreover, our proposed sampling strategy outperforms other baselines in terms of the partial area under the receiver operating characteristic score.
- Y. Koizumi, S. Saito, H. Uematsu, and N. Harada, “Optimizing acoustic feature extractor for anomalous sound detection based on neyman-pearson lemma,” in 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017, pp. 698–702.
- Y. Kawaguchi, R. Tanabe, T. Endo, K. Ichige, and K. Hamada, “Anomaly detection based on an ensemble of dereverberation and anomalous sound extraction,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 865–869.
- Y. Koizumi, S. Saito, M. Yamaguchi, S. Murata, and N. Harada, “Batch uniformization for minimizing maximum anomaly score of dnn-based anomaly detection in sounds,” in 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2019, pp. 6–10.
- K. Suefusa, T. Nishida, H. Purohit, R. Tanabe, T. Endo, and Y. Kawaguchi, “Anomalous sound detection based on interpolation deep neural network,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 271–275.
- H. Purohit, R. Tanabe, T. Endo, K. Suefusa, Y. Nikaido, and Y. Kawaguchi, “Deep autoencoding gmm-based unsupervised anomaly detection in acoustic signals and its hyper-parameter optimization,” arXiv preprint arXiv:2009.12042, 2020.
- K. Wilkinghoff and F. Fritz, “On using pre-trained embeddings for detecting anomalous sounds with limited training data,” in 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023, pp. 186–190.
- A. I. Mezza, G. Zanetti, M. Cobos, and F. Antonacci, “Zero-shot anomalous sound detection in domestic environments using large-scale pretrained audio pattern recognition models,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5.
- K. Shimonishi, K. Dohi, and Y. Kawaguchi, “Anomalous Sound Detection Based on Sound Separation,” in Proc. INTERSPEECH 2023, 2023, pp. 2733–2737.
- H. Hojjati and N. Armanfard, “Self-supervised acoustic anomaly detection via contrastive learning,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 3253–3257.
- Z. Shuyang, T. Heittola, and T. Virtanen, “Active learning for sound event classification by clustering unlabeled data,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017, pp. 751–755.
- ——, “An active learning method using clustering and committee-based sample selection for sound event classification,” in 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2018, pp. 116–120.
- M. Meire, J. Zegers, and P. Karsmakers, “Active learning in sound-based bearing fault detection,” in Proceedings of the 8th Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, September 2023, pp. 111–115.
- D. Wu, C.-T. Lin, and J. Huang, “Active learning for regression using greedy sampling,” Information Sciences, vol. 474, pp. 90–105, 2019.
- B. Lin, Z. Yu, F. Huang, and L. Guo, “Pool-based sequential active learning for regression based on incremental cluster center selection,” in 2021 Ninth International Conference on Advanced Cloud and Big Data (CBD). IEEE, 2022, pp. 176–182.
- I. Žliobaitė, A. Bifet, B. Pfahringer, and G. Holmes, “Active learning with drifting streaming data,” IEEE transactions on neural networks and learning systems, vol. 25, no. 1, pp. 27–39, 2013.
- A. Narr, R. Triebel, and D. Cremers, “Stream-based active learning for efficient and adaptive classification of 3d objects,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 227–233.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
- J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4690–4699.
- K. Wilkinghoff, “Sub-cluster AdaCos: Learning representations for anomalous sound detection,” in 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021, pp. 1–8.
- Y. Koizumi, M. Yasuda, S. Murata, S. Saito, H. Uematsu, and N. Harada, “SPIDERnet: Attention network for one-shot anomaly detection in sounds,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 281–285.
- V. Barnabé-Lortie, C. Bellinger, and N. Japkowicz, “Active learning for one-class classification,” in 2015 IEEE 14th international conference on machine learning and applications (ICMLA). IEEE, 2015, pp. 390–395.
- K. Dohi, K. Imoto, N. Harada, D. Niizumi, Y. Koizumi, T. Nishida, H. Purohit, R. Tanabe, T. Endo, and Y. Kawaguchi, “Description and discussion on DCASE 2023 challenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,” Proceedings of Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, 2023.
- K. Dohi, T. Nishida, H. Purohit, R. Tanabe, T. Endo, M. Yamamoto, Y. Nikaido, and Y. Kawaguchi, “MIMII DG: Sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task,” in Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), Nancy, France, November 2022.
- N. Harada, D. Niizumi, D. Takeuchi, Y. Ohishi, M. Yasuda, and S. Saito, “ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Barcelona, Spain, November 2021, pp. 1–5.
- D. Pelleg and A. Moore, “Active learning for anomaly and rare-category detection,” Advances in neural information processing systems, vol. 17, 2004.
- D. Kottke, G. Krempl, and M. Spiliopoulou, “Probabilistic active learning in datastreams,” in Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22-24, 2015. Proceedings 14. Springer, 2015, pp. 145–157.
- D. K. McClish, “Analyzing a portion of the roc curve,” Medical decision making, vol. 9, no. 3, pp. 190–195, 1989.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.