# AI MusiMuse # 012_STYLE_LEARNING.md Version 1.0 --- # Purpose The purpose of Style Learning is to discover the author's compositional habits. Style is never extracted from a single track. Style emerges only after analyzing the complete music collection. The Style Model becomes the primary knowledge source for the AI Composer. --- # Philosophy Music style is not tempo key or instruments. Style is the statistical behavior of the composer. Examples How long intros usually last. How often themes return. How energy develops. How density changes. How transitions occur. How harmony evolves. --- # Pipeline ``` Composition Dataset ↓ Statistics ↓ Patterns ↓ Habits ↓ Style Model ``` --- # Input The Style Builder receives Composition Samples Timeline Sections Transitions Curves MusicDNA Decision History --- # Output The Style Builder produces Style Model The model contains no audio. Only musical knowledge. --- # Style Categories The model is divided into independent domains. Structure Harmony Rhythm Dynamics Energy Density Texture Transitions Development Ending --- # Structure Statistics Examples Average intro duration Average track duration Average section duration Average section count Typical section order Typical climax position Typical outro duration --- # Harmony Statistics Examples Preferred keys Preferred modes Typical harmonic movement Average modulation frequency Chord complexity Tonal stability --- # Rhythm Statistics Examples Preferred BPM Preferred tempo range Typical beat density Rhythm regularity Syncopation frequency --- # Dynamics Examples Average crest factor Average dynamic range Preferred loudness Typical headroom Preferred silence ratio --- # Energy Examples Average energy curve Maximum energy position Average energy growth Average energy decline Typical climax duration --- # Density Examples Average layer count Maximum density Density evolution Typical density growth Average density reduction --- # Texture Examples Pad frequency Drone frequency Noise frequency Bass frequency Lead frequency Texture transitions --- # Transitions Examples Build Drop Fade Expansion Reduction Silence Each transition stores probability. --- # Development The model learns How ideas evolve. Example ``` Atmosphere ↓ Bass ↓ Theme ↓ Development ↓ Break ↓ Return ↓ Ending ``` The probabilities are stored. --- # Ending Examples Fade out Abrupt ending Long ambient ending Return to atmosphere Energy decay --- # Decision Statistics Every decision stores ``` State ↓ Decision ↓ Probability ``` Example ``` Current Energy 0.42 ↓ Add Bass 78% ``` --- # Correlations The model also stores relationships. Example ``` If Energy > 0.65 ↓ Bass already exists 92% ``` Another example ``` If Break occurred ↓ Theme returns 81% ``` --- # Style Confidence Every learned statistic has confidence sample count variance This allows stable learning even with relatively small libraries. --- # Outliers Rare decisions are preserved. They are never discarded. They simply receive lower probability. Rare events increase creativity. --- # Style Evolution Style Models are versioned. ``` Style v1 ↓ Style v2 ↓ Style v3 ``` The Composer always knows which version generated a track. --- # Learning Strategy The Style Builder never averages songs blindly. Instead it discovers patterns relationships decision probabilities structural habits --- # Composer Integration The Composer never asks "What happened in Track 17?" Instead it asks "What usually happens when the current musical state looks like this?" The Style Model answers with probabilities. --- # Design Principles No audio stored. No MIDI stored. No instruments stored. Only musical knowledge. Style is statistical. Style is versioned. Style is deterministic. --- # Long-Term Goal The Style Model should eventually represent the author's musical thinking. The AI Composer should therefore compose new music using learned habits, while remaining free to create previously unseen combinations. The result should sound like it belongs to the same artistic universe, without reproducing any existing composition.