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René Heinrich

2 accepted papers

2026

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

ICLR 2026poster

Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A key reason is that global pooling creates an information bottleneck causing linear probes to misrepresent the embedding qu…

Cited by 0SourceScholar
2025

BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics

ICLR 2025spotlight

Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and…