Beyond task performance: Decoding bioacoustic embeddings with speech features
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Computer Science > Machine Learning
Title:Beyond task performance: Decoding bioacoustic embeddings with speech features
Abstract:Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task. This hinders transparency and limits extension to rare species or data-scarce domains. Here we reveal which speech-like features are encoded in bioacoustic representations. Using the 88~eGeMAPS features across six taxonomic groups, we apply linear and nonlinear regression probes to quantify which acoustic properties each model captures. Results confirm a ``no free lunch'' pattern: no single model captures the full feature space. A concatenated embedding achieves the highest performance, suggesting complementary acoustic space coverage across models. Loudness features are best encoded ($R^2 = 0.76$) while F0 is hardest to recover ($R^2 = 0.33$). By cross-referencing recoverability with per-species feature salience (NMI), we derive data-driven model selection guidance for bioacoustics.
| Comments: | Accepted at Interspeech 2026 |
| Subjects: | Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2606.14662 [cs.LG] |
| (or arXiv:2606.14662v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14662
arXiv-issued DOI via DataCite (pending registration)
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