Bird MAE Base

--- datasets: - DBD-research-group/BirdSet pipeline_tag: audio-classification library_name: transformers tags: - audio-classification - audio --- # Disclaimer: There might be some error in the models, we have to check it. # Bird-MAE-Base: Can Masked Autoencoders Also Listen to Birds? - **Paper**: [ArXiv](https://arxiv.org/abs/2504.12880) - **Repo**: [GitHub](https://github.com/DBD-research-group/Bird-MAE) ## Abstract Masked Autoencoders (MAEs) have shown competitive results in audio classification by learning rich semantic representations through an efficient self-supervised reconstruction task. However, general-purpose models fail to generalize well when applied directly to fine-grained audio domains. Specifically, bird-sound classification requires distinguishing subtle inter-species differences and managing high intra-species acoustic variability, thereby revealing the performance limitations of general-domain Audio-MAE models. This work demonstrates that bridging this domain gap requires more than domain-specific pretraining data; adapting the entire training pipeline is crucial. We systematically revisit and adapt the pretraining recipe, fine-tuning methods, and frozen feature utilization to bird sounds using BirdSet, a large-scale bioacoustic dataset comparable to AudioSet. Our resulting Bird-MAE achieves new state-of-the-art results in BirdSet's multi-label classification benchmark. Additionally, we introduce the parameter-efficient prototypical probing, enhancing the utility of frozen MAE representations and closely approaching fine-tuning performance in low-resource settings. Bird-MAE's prototypical probes outperform linear probing by up to 37%_\text{p} in MAP and narrow the gap to fine-tuning to approximately 3.3%_\text{p} on average across BirdSet downstream tasks. Bird-MAE also demonstrates robust few-shot capabilities with prototypical probing in our newly established few-shot benchmark on BirdSet, highlighting the potential of tailored self-supervised learning pipelines for fine-grained audio domains. ### Evaluation Results **Table 1** Probing results on the multi-label classification benchmark BirdSet with full data (MAP%). Comparison of linear probing vs. prototypical probing using frozen encoder representations. Models follow the evaluation protocol of BirdSet. **Best** and results are highlighted. | Model | Arch. | Probing | HSN...

Source context: 1797 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo DBD-research-group/Bird-MAE-Base