Retrieval-Augmented Bioacoustics: Evidence-Guided Generation for Animal Communication

Bodhisatta Maiti

Keywords: Bioacoustics, Animal communication, Retrieval-augmented generation, Self-supervised audio representation, Generative modeling, Explainable AI

Abstract: Animal vocalizations carry important information about communication, context, and behavior, but most current AI approaches in bioacoustics focus on narrow tasks such as species classification or call detection. A gap remains in methods that can help researchers interpret and summarize acoustic data in a grounded and transparent way. This proposal introduces Retrieval-Augmented Bioacoustics (RAB), a framework that combines acoustic embeddings with retrieval from call libraries and generative modeling. Retrieval provides concrete evidence, while generation produces outputs such as annotation suggestions, monitoring summaries, cross-species communication hypotheses, and prototype call synthesis. Two design choices strengthen the framework: adapting the number of retrieved neighbors depending on signal quality, and citing retrieved calls directly in generated outputs to increase transparency. RAB offers a model-agnostic approach that can be applied on top of existing or future embedding models, with potential impact on both ethological research and conservation applications.

Submission Number: 45