From Physics to Representation: Audio Learning with Synthetic Pre-training via Procedural Generation
Self-supervised learning advances audio representation for multimedia analysis. However, prevailing data-centric approaches rely on massive real-world corpora, increasing training costs, curation burdens, and privacy barriers. To address this, we present AudioPG, a procedural synthesis framework eliminating real audio recordings during pre-training. AudioPG trains a Transformer-based masked autoencoder on waveforms generated on-the-fly from basic acoustic primitives and composition rules. The encoder transfers effectively to real audio benchmarks, achieving 90.60% accuracy on ESC-50, 0.546 mAP on FSD50K, 88.17% on UrbanSound8K, and 97.03% on Speech Commands V2. Notably, pre-training compl...
| Authors | Fengrui Liu, Ruiyang Huang, Qijian Zheng et al. |
|---|---|
| Primary category | eess.AS |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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