BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics

• URL: http://arxiv.org/abs/2312.07439v2
• Date: Wed, 13 Dec 2023 15:48:17 GMT
• Title: BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
• Authors: Jenny Hamer, Eleni Triantafillou, Bart van Merri\"enboer, Stefan Kahl, Holger Klinck, Tom Denton, Vincent Dumoulin
• Abstract summary: We present BIRB, a complex benchmark centered on the retrieval of bird vocalizations from passively-recorded datasets. We propose a baseline system for this collection of tasks using representation learning and a nearest-centroid search.
• Score: 7.68184437595058
• License: http://creativecommons.org/licenses/by/4.0/
• 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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