From Physics to Representation: Audio Learning with Synthetic Pre-training via Procedural Generation
arXiv:2606.14791v1 Announce Type: cross Abstract: 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 benchmar
The increasing costs and privacy concerns associated with massive real-world audio datasets are driving innovation in synthetic data generation for AI pre-training.
This development offers a potential pathway to significantly reduce reliance on real-world data for AI development, lowering barriers to entry and mitigating privacy risks.
AI models for audio analysis can now be pre-trained without extensive real-world recordings, potentially accelerating development and decentralizing AI capabilities.
- · AI researchers and developers
- · Smaller AI start-ups
- · Industries with sensitive audio data
- · Large data aggregators
- · Companies reliant on exclusive real-world audio datasets
Reduced compute and storage costs for pre-training audio AI models.
Democratization of audio AI development, enabling more diverse applications and smaller players.
Enhanced privacy by design for audio AI systems, potentially accelerating adoption in sensitive sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG