SSL-Net: A Synergistic Spectral and Learning-based Network for Efficient Bird Sound Classification (2309.08072v2)
Abstract: Efficient and accurate bird sound classification is of important for ecology, habitat protection and scientific research, as it plays a central role in monitoring the distribution and abundance of species. However, prevailing methods typically demand extensively labeled audio datasets and have highly customized frameworks, imposing substantial computational and annotation loads. In this study, we present an efficient and general framework called SSL-Net, which combines spectral and learned features to identify different bird sounds. Encouraging empirical results gleaned from a standard field-collected bird audio dataset validate the efficacy of our method in extracting features efficiently and achieving heightened performance in bird sound classification, even when working with limited sample sizes. Furthermore, we present three feature fusion strategies, aiding engineers and researchers in their selection through quantitative analysis.
- “A successful crowdsourcing approach for bird sound classification,” Citizen science, vol. 8, no. 1, 2023.
- Xeno-Canto Foundation, “Xeno-canto: Sharing bird soundsfrom around the world,” https://xeno-canto.org/.
- “On-bird sound recordings: automatic acoustic recognition of activities and contexts,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 6, pp. 1193–1206, 2017.
- Emily Vera Hockman, “Acoustic monitoring of wildlife in inaccessible areas and automatic detection of bird songs from continuous recordings,” 2018.
- “Large-scale multi-modal pre-trained models: A comprehensive survey,” Machine Intelligence Research, pp. 1–36, 2023.
- “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
- “Beats: Audio pre-training with acoustic tokenizers,” arXiv preprint arXiv:2212.09058, 2022.
- “Leaf: A learnable frontend for audio classification,” ICLR, 2021.
- “An efficient model for a vast number of bird species identification based on acoustic features,” Animals, vol. 12, no. 18, pp. 2434, 2022.
- “Handcrafted features and late fusion with deep learning for bird sound classification,” Ecological Informatics, vol. 52, pp. 74–81, 2019.
- “Multileveled ternary pattern and iterative relieff based bird sound classification,” Applied Acoustics, vol. 176, pp. 107866, 2021.
- “An ensemble of convolutional neural networks for audio classification,” Applied Sciences, vol. 11, no. 13, pp. 5796, 2021.
- “Classify bird species audio by augment convolutional neural network,” in 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2022, pp. 1–6.
- “Classification of bird sound using high-and low-complexity convolutional neural networks,” Traitement du Signal, vol. 39, no. 1, pp. 187, 2022.
- A Noumida and Rajeev Rajan, “Multi-label bird species classification from audio recordings using attention framework,” Applied Acoustics, vol. 197, pp. 108901, 2022.
- “Bird call classification using dnn-based acoustic modelling,” Circuits, Systems, and Signal Processing, pp. 1–12, 2022.
- “Sliding-window based scale-frequency map for bird sound classification using 2d-and 3d-cnn,” Expert Systems with Applications, vol. 207, pp. 118054, 2022.
- “Panns: Large-scale pretrained audio neural networks for audio pattern recognition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2880–2894, 2020.
- “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
- “Categorical reparameterization with gumbel-softmax,” arXiv preprint arXiv:1611.01144, 2016.
- “Western mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset,” arXiv preprint arXiv:2207.05393, 2022.