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arXiv · 2312.07439

BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics

Jenny Hamer·Eleni Triantafillou·Bart van Merriënboer·Stefan Kahl·Holger Klinck·Tom Denton·Vincent Dumoulin
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Abstract

The ability for a machine learning model to cope with differences in training and deployment conditions--e.g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases. However, most empirical work in this area has focused on the image domain with artificial benchmarks constructed to measure individual aspects of generalization. We present BIRB, a complex benchmark centered on the retrieval of bird vocalizations from passively-recorded datasets given focal recordings from a large citizen science corpus available for training. We propose a baseline system for this collection of tasks using representation learning and a nearest-centroid search. Our thorough empirical evaluation and analysis surfaces open research directions, suggesting that BIRB fills the need for a more realistic and complex benchmark to drive progress on robustness to distribution shifts and generalization of ML models.

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Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer, Stefan Kahl, Holger Klinck, Tom Denton, Vincent Dumoulin. 2023-12-12. BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics. https://arxiv.org/abs/2312.07439

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