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Music Pipeline Architecture - Placement & Integration Strategy

``` comp-core/ ├── core/ │ ├── cc-core/ # JAX/Flax equilibrium algorithms (motion processing) │ ├── cc-ml/ # ML models & data pipeline ← MUSIC ALREADY HERE │ │ └── data_pipeline/ │ │ ├── downloaders/ # YouTube, music list processing │ │ ├── pipeline/ # music_pipeline.py, parallel_pipeline.py │ │ ├── processors/ # Audio processing │ │ └── storage/ # Local music database │ └── cc-trajectory/ # Trajectory prediction (4GB) │ ├── apps/ │ └── desktop/ │ └── cc-echelon/ # Rust music control (879MB) │ └── crates/ # 20+ Rus

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**Date**: 2025-12-17 **Decision**: Where should the music analysis pipeline live? 1. **Python (cc-ml/data_pipeline)**: Download, conversion, storage 2. **Rust (cc-echelon)**: Real-time audio engine, media handling, phrase intelligence **Why Python:** - librosa, essentia, aubio are Python libraries - Heavy DSP/ML computation (not latency-critical) - Easy integration with existing download pipeline - Rich ecosystem for audio analysis ### Phase 3-4 (Smart Organization & Playlists) → `apps/desktop/cc-echelon/crates/music-brain/` **Why Rust:** - Real-time playlist generation (latency-critical) - Integration with existing audio-engine - Efficient data structures (BTreeMap for key lookups) - Multi-threading for playlist search - Export to binary DJ software formats

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