Music Attribution Scaffold

Music metadata is notoriously fragmented and inaccurate, posing a significant challenge for creator rights and income, especially with the surge of generative AI. This open-source scaffold provides essential infrastructure to unify disparate music metadata, enabling confidence-scored attribution records, transparent provenance, and machine-readable consent for AI training. It addresses the critical need to accurately identify creators and their contributions in an era of rapidly evolving music creation and consumption.

Key Features

Use Cases