Pretrained audio models encode different acoustic features unevenly; combining multiple models or selecting them based on task-specific feature importance improves bioacoustics applications, especially for rare species where data is scarce.
This paper investigates what acoustic features are captured by pretrained audio embedding models used in bioacoustics research. By testing six models across different animal species using 88 speech-derived acoustic features, the authors show that no single model captures all important features—loudness is well-encoded while pitch (F0) is difficult to recover.