Exploring bat song syllable representations in self-supervised audio encoders
<|reference_start|>Exploring bat song syllable representations in self-supervised audio encoders: 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.<|reference_end|>
@article{kloots2024exploring,
    title={Exploring bat song syllable representations in self-supervised audio
  encoders},
    author={Marianne de Heer Kloots, Mirjam Kn"ornschild},
    journal={Proceedings of the 4th International Workshop on Vocal
  Interactivity in-and-between Humans, Animals and Robots (VIHAR), Kos, GR, 6-9
  Sept 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12634},
    primaryClass={cs.SD cs.AI cs.LG eess.AS}
}