Can Masked Autoencoders Also Listen to Birds?

Masked Autoencoders (MAEs) learn rich representations in audio classification through an efficient self-supervised reconstruction task. Yet, general-purpose models struggle in fine-grained audio domains such as bird sound classification, which demands distinguishing subtle inter-species differences under high intra-species variability. We show that bridging this domain gap requires full-pipeline adaptation beyond domain-specific pretraining data. Using BirdSet, a large-scale bioacoustic benchmark, we systematically adapt pretraining, fine-tuning, and frozen feature utilization. Our Bird-MAE sets new state-of-the-art results on BirdSet’s multi-label classification benchmark. Additionally, we introduce the parameterefficient prototypical probing, which boosts the utility of frozen MAE features by achieving up to 37 mAP points over linear probes and narrowing the gap to fine-tuning in low-resource settings. Bird-MAE also exhibits strong few-shot generalization with prototypical probes on our newly established few-shot benchmark on BirdSet, underscoring the importance of tailored self-supervised learning pipelines for fine-grained audio domains.