Multi-modal Language models in bioacoustics with zero-shot transfer: a case study.

ABSTRACT: Automatically detecting sound events with Artificial Intelligence (AI) has become increas- ingly popular in the field of bioacoustics, ecoacoustics, and soundscape ecology, particularly for wildlife monitoring and conservation. Conventional methods predominantly employ supervised learning techniques that depend on substantial amounts of manually annotated bioacoustic data. However, manual annotation in bioacoustics is tremendously resource- intensive in terms of both human labor and financial resources, and it requires considerable domain expertise. Moreover, the supervised learning framework limits the application scope to predefined categories within a closed setting. The recent advent of Multi-Modal Language Models has markedly enhanced the versatility and possibilities within the realm

SUBMITTER: Miao Z 

PROVIDER: S-EPMC11871236 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature