DJ builds comprehensive AI music tagging system combining metadata, audio analysis, and machine learning to organize 10,000-track library

Project evolved from a simple Python script using ChatGPT to process metadata into a full-featured application after friends requested help organizing their music libraries

Multi-signal approach combines Discogs-based 200M record database, audio DSP analysis (BPM, harmonic content, energy), and trained ML models to overcome limitations of any single method

Mac companion app manages library tagging across four dimensions (50+ sub-genres, region, era, vibe) and syncs directly to Rekordbox DJ software

Hybrid system handles both well-known and obscure tracks by cross-referencing metadata, analyzing audio directly, and using multiple AI models for different classification tasks