Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners
Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes to reach competitive performance. A markedly different pre-training philosophy underpins the most influential progress in language modeling and, more recently, in visual representation learning: rather than train encoders as static feature extractors, models are trained to predict the next element, a discrete token or a continuous embedding, from the preceding context. Autoregressive prediction thereby provides a unified pre-training interface that transfers across modalities, compelling the model to learn the u...
| Authors | Umberto Cappellazzo, Xubo Liu, Stavros Petridis et al. |
|---|---|
| Primary category | eess.AS |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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