Music Attribution Scaffold

About

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

• Multi-source ETL for comprehensive music metadata ingestion.
• Calibrated confidence scoring with conformal prediction for reliable attribution.
• Integrated Model Context Protocol (MCP) server for machine-readable AI training consent.
• 0 GitHub stars
• Advanced 5-strategy entity resolution for accurate artist identification.
• Agentic UI and editorial frontend for natural-language queries and review.

Use Cases

• Accurately attributing music creators and rights holders in generative AI contexts.
• Establishing transparent digital provenance and trust for musical works.
• Managing and querying machine-readable consent for AI training data usage.