MetaPerch: Learning from metadata for bioacoustics foundation models
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 repres
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- PossiblePossibly related (embedding) · 46%Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data - Frontiers →
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “MetaPerch: Learning from metadata for bioacoustics foundatio” ≈ “amitness/learning””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “MetaPerch: Learning from metadata for bioacoustics foundatio” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Mustafa Chasmai →
“MetaPerch: Learning from metadata for bioacoustics foundation models”
- LinkedLinked via arxiv author · 85%Vincent Dumoulin →
“MetaPerch: Learning from metadata for bioacoustics foundation models”
- LinkedLinked via arxiv author · 85%Jenny Hamer →
“MetaPerch: Learning from metadata for bioacoustics foundation models”