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

BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics