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Can Masked Autoencoders Also Listen to Birds?

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arxiv 2504.12880 v4 pith:P23XUSCG submitted 2025-04-17 cs.LG cs.SDeess.AS

Lukas Rauch , René Heinrich , Ilyass Moummad , Alexis Joly , Bernhard Sick , Christoph Scholz This is my paper · ORCID
classification cs.LGcs.SDeess.AS
keywords birdsetaudiobird-maeclassificationfine-tuningprobingprototypicalautoencoders
verification ladder T0 review T1 audit T2 compute T3 formal
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Masked Autoencoders (MAEs) learn rich semantic representations in audio classification 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, revealing the performance limitations of general-domain Audio-MAEs. This work demonstrates that bridging this domain gap domain gap requires full-pipeline adaptation, not just domain-specific pretraining data. 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 percentage points in mean average precision and narrow the gap to fine-tuning 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.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. A Self-Supervised Approach for Minimal-Annotation Hydroacoustic Data Exploration

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    Event-level MAE embeddings plus UMAP/HDBSCAN or K-Means clustering recover 15 hydroacoustic classes from multi-year Mayotte data with ~1 hour of annotation and detector-comparable F1.

  3. Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification

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    A binarized prototypical probing method that pools per-class evidence from patch tokens substantially outperforms [cls]-token and attentive probes on multi-label audio classification benchmarks.

  4. Adversarial Training Improves Generalization Under Distribution Shifts in Bioacoustics

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Output-space adversarial training improved clean-data performance and adversarial robustness of two bird sound classifiers across seven soundscape test sets, and stabilized prototype-based explanations.

  5. Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF+ 2026

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    For BirdCLEF+ 2026, a frozen Perch-v2 probe plus a trained HGNetV2-B0 SED net and non-bird prototype heads reach private LB 0.936, while WavTokenizer codec tokens collapse and four general audio transformers lag under...

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  7. Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types

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    On WiWa, factorised multi-task bird species and call-type classification with adaptive loss balancing improves call-type recognition most consistently, while preferred weighting and adaptation depth depend on backbone...

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