MetaPerch: Learning from metadata for bioacoustics foundation models

• URL: http://arxiv.org/abs/2607.14072v1
• Date: Wed, 15 Jul 2026 17:42:41 GMT
• Title: MetaPerch: Learning from metadata for bioacoustics foundation models
• Abstract summary: We present an empirical study of the effects of 9 diverse metadata sources on 17 bioacoustic datasets.<n>We introduce MetaPerch, a new foundation model that achieves strong species identification performance across multiple challenging domains.
• Score: 4.5242726521122405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent work has shown that supervision alone can produce SotA species detection models when trained on this large-scale data -- however, there remains unutilized potential in the form of recording metadata readily available within these community-driven data hubs. In this work, we explore the use of metadata -- such as location and time -- as auxiliary supervision signals, allowing the model to leverage species-metadata correlations in its learned representation. Auxiliary metadata losses provide additional information beyond vocalizations alone that can encourage a richer, more robust representation that generalizes better to species distribution and acoustic domain shifts -- important challenges for deployment in real-world passive acoustic monitoring (PAM) settings. We introduce MetaPerch, a new foundation model that achieves strong species identification performance across multiple challenging domains and present an extensive empirical study of the effects of 9 diverse metadata sources on 17 bioacoustic datasets.

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