eGeMAPS
egemaps-7d141504·2 events·first seen 9d agoAliases: eGeMAPS
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Probing bioacoustic embeddings for speech-like acoustic features reveals no-free-lunch pattern
A new arXiv preprint investigates which acoustic features are encoded in pretrained bioacoustic audio embeddings using 88 eGeMAPS speech features across six taxonomic groups. Linear and nonlinear regression probes reveal that no single model captures the full acoustic feature space, with loudness best recovered (R²=0.76) and fundamental frequency hardest (R²=0.33). A concatenated embedding approach achieves highest overall performance, suggesting complementary coverage across models. The work provides data-driven model selection guidance for bioacoustics tasks involving rare species or low-resource domains.
Acoustic cue alignment tokens improve speech emotion recognition in audio language models
Researchers study whether instruction-following audio language models (ALMs) use explicit acoustic cues in a grounded way when raw audio is already available. They derive six interpretable acoustic concept tokens from the eGeMAPS feature set and append them to text prompts, testing on FAU-Aibo and IEMOCAP benchmarks. Aligned tokens improve unweighted average recall while shuffled or corrupted tokens degrade performance, but models don't fully collapse under perturbation, indicating partial anchoring to the audio signal. The work offers a practical probing method for interpretability and robustness in affective computing with ALMs.