Exploring bat song syllable representations in self-supervised audio encoders
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912034915876864 |
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| author | Kloots, Marianne de Heer Knörnschild, Mirjam |
| author_facet | Kloots, Marianne de Heer Knörnschild, Mirjam |
| contents | How well can deep learning models trained on human-generated sounds distinguish between another species' vocalization types? We analyze the encoding of bat song syllables in several self-supervised audio encoders, and find that models pre-trained on human speech generate the most distinctive representations of different syllable types. These findings form first steps towards the application of cross-species transfer learning in bat bioacoustics, as well as an improved understanding of out-of-distribution signal processing in audio encoder models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12634 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring bat song syllable representations in self-supervised audio encoders Kloots, Marianne de Heer Knörnschild, Mirjam Sound Artificial Intelligence Machine Learning Audio and Speech Processing How well can deep learning models trained on human-generated sounds distinguish between another species' vocalization types? We analyze the encoding of bat song syllables in several self-supervised audio encoders, and find that models pre-trained on human speech generate the most distinctive representations of different syllable types. These findings form first steps towards the application of cross-species transfer learning in bat bioacoustics, as well as an improved understanding of out-of-distribution signal processing in audio encoder models. |
| title | Exploring bat song syllable representations in self-supervised audio encoders |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.12634 |