| name | federation-music |
| description | Combine GA music theory tools with ix ML algorithms for music analysis |
Federation Music Analysis
Cross-repo music analysis combining GA's 50+ music theory tools with ix's ML capabilities.
When to Use
When the user needs ML-powered music analysis — clustering chord progressions, spectral analysis, pattern detection in music data.
Quick Start
Call ix_ga_bridge with action=workflow_guide for a complete list of GA-to-ix workflows.
Capabilities
- GA provides: chord parsing (
GaParseChord), progression analysis (GaAnalyzeProgression), atonal set theory (GaChordToSet), scale data (GetAvailableScales), voice leading (GaCommonTones), arpeggio suggestions (GaArpeggioSuggestions)
- ix provides: FFT (
ix_fft), K-Means (ix_kmeans), PCA, supervised classification (ix_supervised), ML pipeline (ix_ml_pipeline), code analysis (ix_code_analyze)
Bridge Tool
ix_ga_bridge converts GA data to ML-ready features:
chord_features — Chord symbols to interval/pitch-class vectors
progression_features — Progression to feature matrix
scale_features — Scale data to binary pitch-class sets
workflow_guide — Show all available GA-to-ix workflows
Example Workflows
Cluster chord voicings by harmonic similarity
GaParseChord — parse chord symbols
GaChordToSet — get pitch-class set + ICV
ix_kmeans — cluster by ICV vectors
Classify progressions by style
GaAnalyzeProgression — detect key, get Roman numerals
GaCommonTones — compute voice-leading distances
ix_ml_pipeline — train style classifier
Analyze harmonic complexity
GaChordToSet — get pitch-class analysis
ix_stats — statistical summary
ix_chaos_lyapunov — measure harmonic unpredictability
Voice-leading optimization
GaCommonTones — common tones between chords
ix_search — A* shortest path in voice-leading space
ix_optimize — minimize total voice-leading distance
Scale recommendation
GaAnalyzeProgression — detect key
GaArpeggioSuggestions — per-chord arpeggios and modes
ix_supervised — train scale recommender