Exploring bat song syllable representations in self-supervised audio encoders
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 19 Sep 2024.
Builds on
8 of 16 references.
References · 8
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
- 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 293
- 2016FMA: A Dataset For Music Analysiscited by 205
- 2021Computational bioacoustics with deep learning: a review and roadmapcited by 78
- 2023What Do Self-Supervised Speech Models Know About Words?cited by 62
- 2022AVES: Animal Vocalization Encoder based on Self-Supervisioncited by 53
- 2024Human-like Linguistic Biases in Neural Speech Models: Phonetic Categorization and Phonotactic Constraints in Wav2Vec2.0cited by 19
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Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processingcited by 9
- 2025Crossing the Species Divide: Transfer Learning from Speech to Animal Soundscited by 4
- 2025An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shiftcited by 0
- 2026Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classificationcited by 0
- 2026Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognitioncited by 0
- 2026Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition8 Sep 2026
- 2026Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification30 Apr 2026
- 2025An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift14 Sep 2025
- 2025Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds4 Sep 2025
- 2025Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing10 Jan 2025