Self-Supervised Learning for Few-Shot Bird Sound Classification
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911774050091008 |
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| author | Moummad, Ilyass Serizel, Romain Farrugia, Nicolas |
| author_facet | Moummad, Ilyass Serizel, Romain Farrugia, Nicolas |
| contents | Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost. This is pertinent in bioacoustics, where biologists routinely collect extensive sound datasets from the natural environment. In this study, we demonstrate that SSL is capable of acquiring meaningful representations of bird sounds from audio recordings without the need for annotations. Our experiments showcase that these learned representations exhibit the capacity to generalize to new bird species in few-shot learning (FSL) scenarios. Additionally, we show that selecting windows with high bird activation for self-supervised learning, using a pretrained audio neural network, significantly enhances the quality of the learned representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15824 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Learning for Few-Shot Bird Sound Classification Moummad, Ilyass Serizel, Romain Farrugia, Nicolas Sound Machine Learning Audio and Speech Processing Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost. This is pertinent in bioacoustics, where biologists routinely collect extensive sound datasets from the natural environment. In this study, we demonstrate that SSL is capable of acquiring meaningful representations of bird sounds from audio recordings without the need for annotations. Our experiments showcase that these learned representations exhibit the capacity to generalize to new bird species in few-shot learning (FSL) scenarios. Additionally, we show that selecting windows with high bird activation for self-supervised learning, using a pretrained audio neural network, significantly enhances the quality of the learned representations. |
| title | Self-Supervised Learning for Few-Shot Bird Sound Classification |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2312.15824 |