Back to feed
Dev.to
Dev.to
6/26/2026
Algorithmic Entity Resolution in Music Metadata

Algorithmic Entity Resolution in Music Metadata

Short summary

Music streaming platforms track recordings and compositions via separate ISRC/ISWC code systems, trapping millions in unclaimed royalties. The article presents a three-tier entity resolution pipeline using Levenshtein distance, creator overlap checks, and duration tolerance to automatically reconcile metadata. Includes a production Python/Polars implementation that scales across millions of streaming records.

  • •ISRC/ISWC metadata gap in music streaming creates unclaimed royalty problem in CMO Black Boxes
  • •Three-stage matching pipeline: normalized title similarity (≥0.85), creator intersection validation, duration tolerance (±4 seconds)
  • •Runnable Polars implementation with stateful orchestration patterns (LangGraph, vector databases) for production scale

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#open-source
Read full article at Dev.to

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

Why Music Metadata Matching Is Harder Than It Looks
Dev.to
The original title is: "Cross-Lingual Entity Resolution: Why Your Knowledge Graph Has Four Samsungs"
Dev.to
The original title is "Auto-Tag Your Music Library — With Confidence Scores"
Dev.to
The original title is: "Half-Time vs Double-Time BPM Detection: How We Fixed Spotify's Known Accuracy Gap"
Dev.to