Creates algorithmic music composition systems using procedural generation, Markov chains, L-systems, and neural approaches for ambient, adaptive, and experimental music.
Scanned 9/10/2026
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---
name: generative-music-composer
description: Creates algorithmic music composition systems using procedural generation, Markov chains, L-systems, and neural approaches for ambient, adaptive, and experimental music.
license: MIT
governance_phases: [build]
organ_affinity: [organ-ii]
triggers: [user-asks-about-algorithmic-music, user-asks-about-composition, context:music-generation]
---
# Generative Music Composer
This skill provides guidance for creating algorithmic and procedural music systems that generate compositions autonomously or semi-autonomously.
## Core Competencies
- **Algorithmic Composition**: Rule-based music generation
- **Stochastic Methods**: Markov chains, probability distributions
- **Formal Grammars**: L-systems, generative grammars for music
- **Adaptive Systems**: Music that responds to input/context
- **Neural Approaches**: ML-based generation techniques
## Generative Music Fundamentals
### Generation Paradigms
| Approach | Description | Best For |
|----------|-------------|----------|
| Rule-based | Explicit compositional rules | Traditional styles, controlled output |
| Stochastic | Probability-driven selection | Natural variation, surprise |
| Grammar-based | Recursive structure generation | Complex forms, self-similarity |
| Constraint-based | Satisfy musical constraints | Harmony, voice leading |
| Learning-based | Train on corpus | Style imitation, novelty |
### Musical Elements to Generate
```
┌─────────────────────────────────────────────────────────┐
│ Music Generation Layers │
├─────────────────────────────────────────────────────────┤
│ │
│ Macro Structure │ Form, sections, key areas │
│ ──────────────────────────────────────────────────────│
│ Harmony │ Chord progressions, voice leading │
│ ──────────────────────────────────────────────────────│
│ Melody │ Pitch sequences, contour, rhythm │
│ ──────────────────────────────────────────────────────│
│ Rhythm │ Duration patterns, meter, groove │
│ ──────────────────────────────────────────────────────│
│ Timbre/Texture │ Instrumentation, dynamics │
│ ──────────────────────────────────────────────────────│
│ Micro Variation │ Ornaments, expression, humanize │
│ │
└─────────────────────────────────────────────────────────┘
```
## Stochastic Generation
### Markov Chain Melody
```python
import random
from collections import defaultdict
class MarkovMelodyGenerator:
"""Generate melodies using Markov chains"""
def __init__(self, order=2):
self.order = order
self.transitions = defaultdict(list)
def train(self, melodies):
"""Learn from existing melodies (lists of MIDI notes)"""
for melody in melodies:
for i in range(len(melody) - self.order):
state = tuple(melody[i:i + self.order])
next_note = melody[i + self.order]
self.transitions[state].append(next_note)
def generate(self, length, seed=None):
"""Generate a new melody"""
if seed is None:
seed = random.choice(list(self.transitions.keys()))
melody = list(seed)
for _ in range(length - self.order):
state = tuple(melody[-self.order:])
if state in self.transitions:
next_note = random.choice(self.transitions[state])
else:
# Fallback: random from all possible next notes
next_note = random.choice(
[n for notes in self.transitions.values() for n in notes]
)
melody.append(next_note)
return melody
```
### Weighted Random Selection
```python
def weighted_choice(options, weights):
"""Select with custom probability distribution"""
total = sum(weights)
r = random.uniform(0, total)
cumulative = 0
for option, weight in zip(options, weights):
cumulative += weight
if r <= cumulative:
return option
return options[-1]
# Scale degree probabilities (tendency tones)
scale_weights = {
1: 0.20, # Tonic - stable, common
2: 0.10, # Supertonic - passing
3: 0.15, # Mediant - stable
4: 0.10, # Subdominant - tendency to 3
5: 0.20, # Dominant - stable
6: 0.10, # Submediant - relative major/minor
7: 0.05, # Leading tone - strong tendency
8: 0.10 # Octave
}
def generate_scale_melody(length, key='C', scale='major'):
degrees = list(scale_weights.keys())
weights = list(scale_weights.values())
melody = [weighted_choice(degrees, weights) for _ in range(length)]
return [degree_to_midi(d, key, scale) for d in melody]
```
## Grammar-Based Generation
### L-System for Musical Structure
```python
class MusicalLSystem:
"""L-system for generating musical phrases"""
def __init__(self):
self.rules = {
'A': 'AB', # Antecedent expands to Antecedent + Bridge
'B': 'CA', # Bridge expands to Consequent + Antecedent
'C': 'DC', # Consequent expands to Development + Consequent
'D': 'A' # Development returns to Antecedent
}
self.interpretations = {
'A': self._phrase_a,
'B': self._phrase_b,
'C': self._phrase_c,
'D': self._phrase_d
}
def generate_structure(self, axiom='A', iterations=4):
"""Generate formal structure"""
result = axiom
for _ in range(iterations):
result = ''.join(self.rules.get(c, c) for c in result)
return result
def realize(self, structure):
"""Convert structure to musical phrases"""
phrases = []
for symbol in structure:
if symbol in self.interpretations:
phrases.append(self.interpretations[symbol]())
return phrases
def _phrase_a(self):
# Antecedent: tension-building phrase
return generate_phrase(contour='ascending', cadence='half')
def _phrase_b(self):
# Bridge: transitional material
return generate_phrase(contour='static', cadence='deceptive')
def _phrase_c(self):
# Consequent: resolution phrase
return generate_phrase(contour='descending', cadence='authentic')
def _phrase_d(self):
# Development: variation material
return generate_phrase(contour='varied', cadence='half')
```
### Generative Grammar for Rhythm
```python
class RhythmGrammar:
"""Context-free grammar for rhythm generation"""
def __init__(self):
# Non-terminal symbols with production rules
self.rules = {
'MEASURE': [
['HALF', 'HALF'],
['QUARTER', 'QUARTER', 'QUARTER', 'QUARTER'],
['DOTTED_HALF', 'QUARTER'],
['BEAT', 'BEAT', 'BEAT', 'BEAT']
],
'HALF': [
['QUARTER', 'QUARTER'],
['half']
],
'QUARTER': [
['EIGHTH', 'EIGHTH'],
['quarter'],
['SIXTEENTH', 'SIXTEENTH', 'EIGHTH']
],
'BEAT': [
['quarter'],
['EIGHTH', 'EIGHTH'],
['TRIPLET']
],
'EIGHTH': [
['eighth'],
['SIXTEENTH', 'SIXTEENTH']
],
'TRIPLET': [
['triplet', 'triplet', 'triplet']
],
'SIXTEENTH': [
['sixteenth']
]
}
def generate(self, symbol='MEASURE'):
"""Recursively expand grammar"""
if symbol not in self.rules:
return [symbol] # Terminal symbol
# Choose random production rule
production = random.choice(self.rules[symbol])
result = []
for s in production:
result.extend(self.generate(s))
return result
```
## Constraint-Based Harmony
### Voice Leading Rules
```python
class HarmonyGenerator:
"""Generate chord progressions with voice leading constraints"""
def __init__(self, key='C', mode='major'):
self.key = key
self.mode = mode
self.chord_vocabulary = self._build_chords()
def generate_progression(self, length=8):
"""Generate progression satisfying constraints"""
progression = [self._tonic_chord()] # Start on tonic
for _ in range(length - 2):
current = progression[-1]
candidates = self._valid_next_chords(current)
next_chord = self._select_chord(candidates, current)
progression.append(next_chord)
# End with cadence
progression.append(self._dominant_chord())
progression.append(self._tonic_chord())
return progression
def _valid_next_chords(self, current):
"""Filter chords by voice leading constraints"""
candidates = []
for chord in self.chord_vocabulary:
if self._check_voice_leading(current, chord):
candidates.append(chord)
return candidates
def _check_voice_leading(self, chord1, chord2):
"""Check voice leading rules"""
# No parallel fifths
if self._has_parallel_fifths(chord1, chord2):
return False
# No parallel octaves
if self._has_parallel_octaves(chord1, chord2):
return False
# Resolve tendency tones
if not self._resolves_tendencies(chord1, chord2):
return False
# Limit voice movement
if self._excessive_movement(chord1, chord2):
return False
return True
```
### Functional Harmony
```python
# Chord function probabilities
PROGRESSION_TENDENCIES = {
'I': {'IV': 0.3, 'V': 0.3, 'vi': 0.2, 'ii': 0.1, 'iii': 0.1},
'ii': {'V': 0.7, 'vii': 0.2, 'IV': 0.1},
'iii': {'vi': 0.4, 'IV': 0.3, 'ii': 0.2, 'I': 0.1},
'IV': {'V': 0.4, 'I': 0.2, 'ii': 0.2, 'vii': 0.1, 'vi': 0.1},
'V': {'I': 0.6, 'vi': 0.3, 'IV': 0.1},
'vi': {'IV': 0.3, 'ii': 0.3, 'V': 0.2, 'I': 0.1, 'iii': 0.1},
'vii': {'I': 0.5, 'iii': 0.3, 'vi': 0.2}
}
def generate_functional_progression(length):
progression = ['I']
for _ in range(length - 1):
current = progression[-1]
tendencies = PROGRESSION_TENDENCIES[current]
next_chord = weighted_choice(
list(tendencies.keys()),
list(tendencies.values())
)
progression.append(next_chord)
return progression
```
## Adaptive and Interactive Music
### Parameter-Driven Generation
```python
class AdaptiveComposer:
"""Music that responds to external parameters"""
def __init__(self):
self.parameters = {
'energy': 0.5, # 0-1: calm to intense
'tension': 0.5, # 0-1: consonant to dissonant
'density': 0.5, # 0-1: sparse to dense
'tempo_factor': 1.0 # Tempo multiplier
}
def update_parameter(self, name, value):
self.parameters[name] = max(0, min(1, value))
def generate_measure(self):
"""Generate music adapted to current parameters"""
energy = self.parameters['energy']
tension = self.parameters['tension']
density = self.parameters['density']
# Adapt musical elements
note_density = int(4 + density * 12) # 4-16 notes
velocity_range = (40 + int(energy * 40), 80 + int(energy * 47))
# Tension affects harmony
if tension < 0.3:
chord_pool = ['I', 'IV', 'V', 'vi'] # Consonant
elif tension < 0.7:
chord_pool = ['ii', 'iii', 'IV', 'V', 'vi'] # Mixed
else:
chord_pool = ['ii', 'vii', 'V7', 'bVII', 'iv'] # Tense
# Generate with adapted parameters
return self._generate_notes(
density=note_density,
velocity_range=velocity_range,
harmonic_pool=chord_pool
)
```
### Game Audio Adaptive System
```python
class GameMusicSystem:
"""Layered adaptive music for games"""
def __init__(self):
self.layers = {
'ambient': {'volume': 1.0, 'active': True},
'percussion': {'volume': 0.0, 'active': False},
'melody': {'volume': 0.0, 'active': False},
'intensity': {'volume': 0.0, 'active': False}
}
self.current_state = 'exploration'
def set_game_state(self, state, transition_time=2.0):
"""Crossfade layers based on game state"""
presets = {
'exploration': {
'ambient': 1.0, 'percussion': 0.0,
'melody': 0.3, 'intensity': 0.0
},
'tension': {
'ambient': 0.7, 'percussion': 0.3,
'melody': 0.5, 'intensity': 0.3
},
'combat': {
'ambient': 0.3, 'percussion': 1.0,
'melody': 0.7, 'intensity': 1.0
},
'victory': {
'ambient': 0.5, 'percussion': 0.5,
'melody': 1.0, 'intensity': 0.0
}
}
target = presets.get(state, presets['exploration'])
self._crossfade_to(target, transition_time)
self.current_state = state
```
## Output Formats
### MIDI Generation
```python
from midiutil import MIDIFile
def create_midi(melody, filename='output.mid', tempo=120):
"""Export melody to MIDI file"""
midi = MIDIFile(1) # One track
track = 0
channel = 0
time = 0
volume = 100
midi.addTempo(track, 0, tempo)
for note in melody:
pitch = note['pitch']
duration = note['duration']
midi.addNote(track, channel, pitch, time, duration, volume)
time += duration
with open(filename, 'wb') as f:
midi.writeFile(f)
```
## Best Practices
### Musical Coherence
1. **Repetition with variation**: Repeat themes but vary them
2. **Motivic development**: Transform small ideas
3. **Hierarchical structure**: Phrases → sections → movements
4. **Tension and release**: Build and resolve over time
### Avoiding Common Pitfalls
- Pure randomness sounds chaotic—add constraints
- Too many rules sound mechanical—add stochastic variation
- Test with actual audio, not just data
- Consider performance/playability
## References
- `references/music-theory-primer.md` - Essential music theory for generation
- `references/markov-music.md` - Advanced Markov chain techniques
- `references/midi-reference.md` - MIDI specification and libraries
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