MiCLA: Mixture-to-Constituent Latent Alignment for Polyphonic Audio Representation Learning
Real-world audio typically contains concurrent sound events, yet standard self-supervised methods treat each clip as an isolated training unit without explicitly modeling mixture--constituent relationships. We propose Mixture-to-Constituent Latent Alignment (MiCLA), a two-stage self-supervised pre-training framework that uses synthetic log-mel mixing to align mixture representations with their constituent clips. Stage 1 structures single-clip representations via masked self-distillation and contrastive regularization. Stage 2 introduces bidirectional alignment: for a synthetic mixture, MiCLA suppresses the component aligned with one constituent and aligns the residual with the other, usin...
| Authors | Kohei Yamamoto, Kosuke Okusa |
|---|---|
| Primary category | cs.SD |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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