bacpipe: a Python package to make bioacoustic deep learning models accessible

Bioacoustics and ethology encompass a wide range of audio understanding tasks, many of which stand to benefit from recent advances in large audio-language models. However, progress in the field has so far been assessed on a narrow set of tasks, primarily centered on label-centric biological category recognition, such as species and call-type classification. In this work, we introduce BEANS-Next, a benchmark grounded in a taxonomy of bioacoustics tasks spanning acoustic perception, biological category recognition, scene understanding, and in-context learning. Using BEANS-Next, we show that existing models exhibit limited performance beyond the task families emphasized by existing evaluations, constraining their usefulness for broader bioacoustic applications. To support progress on this broader task space, we also introduce ROOTS, a large-scale training resource built from expanded curated real-world data and previously underused behavioral and acoustic metadata, supplemented by audio-derived information and scalable synthetic generation where labeling is insufficient. We demonstrate that training on this dataset yields substantial progress across all task groups of BEANS-Next, moving audio-language models closer to their potential as general-purpose assistants for bioacoustics and ethology. To accelerate progress in the field, we open-source our benchmark, dataset, and data pipelines.

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.

Bioacoustic foundation models are claimed to generalize across taxa and recording conditions, but are trained mainly on terrestrial archives of focal, high-quality recordings and evaluated on benchmarks emphasizing architecture over data conditions. We evaluate four such models as frozen encoders on an underwater detection task, using linear probing on continuously recorded data from the Kerguelen Plateau. Four datasets of identical size were built to differ only in the signal-to-noise ratio (SNR) of their positive segments, and probes were trained and evaluated across all combinations. Evaluation SNR accounts for more variation in average precision (AP) than model choice, and performance peaks when training and evaluation share a noise regime --- the configuration least representative of a continuous deployment. Training on mixed SNR redistributes performance toward low-SNR conditions without improving it overall, and AP remains low throughout. In light of these results, we call for evaluation standards grounded in realistic noise and deployment conditions rather than curated, high-quality recordings.