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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of a probing framework that quantifies the recoverability of acoustic features from pretrained models, providing insights into model selection and interpretability in bioacoustics. The comprehensive analysis of technical contributions, methodology, and significance to the field underscores its potential to advance understanding and application of machine learning in ecological monitoring.
Methodology
The paper introduces a novel regression probing framework to evaluate the recoverability of acoustic features from pretrained embeddings in bioacoustics. This approach is significant as it shifts the focus from traditional task performance metrics to a more interpretable analysis of what models actually encode. The use of eGeMAPS features across diverse taxa and the combination of linear and nonlinear regression probes provide a robust methodology for assessing model capabilities. However, the methodology could benefit from additional layer-wise analysis to further elucidate the encoding dynamics.
Results & evaluation
The experiments are well-structured, utilizing six bioacoustic datasets and a comprehensive set of 88 eGeMAPS features. The results reveal substantial variation in feature recoverability across models, highlighting the complementary nature of different pretrained embeddings. The analysis of recoverability in conjunction with feature salience (NMI) provides practical insights for model selection, which is particularly valuable in data-scarce domains. The findings are presented clearly, with appropriate visualizations that enhance understanding.
Reproducibility
The paper provides sufficient detail regarding the datasets, feature extraction methods, and model architectures used, which supports reproducibility. The use of established tools like openSMILE and scikit-learn for feature extraction and regression modeling further aids in replicating the study. However, the absence of publicly available code or a demo limits the ease of reproduction.
Limitations
The study acknowledges several limitations, including potential biases in feature extraction from bioacoustic signals and the reliance on eGeMAPS features, which may not fully capture the nuances of non-human vocalizations. Additionally, the pooling of embeddings across time may obscure temporal dynamics, and the lack of layer-wise analysis could overlook important encoding behaviors.
Broader impact
This research has significant implications for bioacoustic monitoring and conservation efforts, as it provides a framework for selecting models that are better suited for specific tasks in diverse ecological contexts. By enhancing the interpretability of model decisions, the findings could facilitate more effective applications in wildlife conservation and ecological research. The main contribution of this paper is the development of a probing framework that quantifies the recoverability of acoustic features from pretrained models, providing insights into model selection and interpretability in bioacoustics. The comprehensive analysis of technical contributions, methodology, and significance to the field underscores its potential to advance understanding and application of machine learning in ecological monitoring.
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