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Use Case: GuitarAlchemist Music ML

Combining GA music theory tools with ix ML algorithms for chord clustering, scale recommendation, genre classification, and progression prediction via MCP federation.

Pipelines

1. Chord Clustering (K-Means)

GA extracts interval vectors (GaChordIntervals), ix clusters by harmonic similarity. Major7s, minor7s, dominant7s group naturally.

2. Scale Recommendation (KNN)

Features: root pitch class, major/minor 3rd, 7th type, tritone presence. KNN recommends matching scale (Ionian, Mixolydian, Dorian, Locrian).

3. Genre Classification (TF-IDF + Naive Bayes)

Chord progressions treated as "text" — each chord is a word. TF-IDF vectorizes, Naive Bayes classifies Jazz vs Pop vs Blues.

4. Progression Prediction (HMM/Viterbi)

Hidden states = chord functions (I, ii, IV, V, vi). Transition matrix encodes common voice leading. Viterbi decodes most likely next chord.

5. MCP Federation

GA: GaParseChord → intervals
GA: GaKeyFromProgression → key detection
ix: ix_kmeans → cluster chords
ix: ix_supervised(knn) → recommend scale
TARS: grammar_evolve → refine rules
ix: ix_governance_check → constitutional compliance

Algorithms Used

Algorithm	ix Crate	Role
K-Means	ix-unsupervised	Chord clustering
KNN	ix-supervised	Scale recommendation
TF-IDF + Naive Bayes	ix-supervised	Genre classification
HMM/Viterbi	ix-graph	Progression prediction

Related

• Guitar Alchemist tutorial
• GA repo