232 lines
5.3 KiB
Markdown
232 lines
5.3 KiB
Markdown
# Granular Synthesis Results
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## Resumen de Integracion de Sintesis Granular
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**Sprint:** Granular v0.1.40
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**Tareas:** T018-T043
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**Modulo:** `spectral_engine.py`
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---
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## Resumen Ejecutivo
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El modulo de sintesisgranular permite seleccion de samples basada en caracteristicas timbricas (espectrales) en lugar de solo metadatos. Esto mejora significativamente la coherencia sonora de las generaciones.
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---
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## Caracteristicas Implementadas
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### T018: Analisis Espectral de Samples
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```python
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@dataclass
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class SpectralProfile:
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path: str
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centroid_mean: float # Centroide espectral promedio
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centroid_std: float # Desviacion del centroide
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rolloff_85: float # Frecuencia de rolloff 85%
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flux_mean: float # Flujo espectral promedio
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mfcc: List[float] # 13 coeficientes MFCC
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rms: float # Energia RMS
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spectral_flatness: float # Planitud espectral
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duration: float # Duracion en segundos
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genre_hints: List[str] # Generos sugeridos
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```
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**Metricas calculadas:**
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- **Centroide espectral**: Brillo del sonido (kick ~250Hz, synth ~2000Hz)
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- **Rolloff 85%**: Frecuencia donde se concentra 85% de la energia
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- **MFCC**: Coeficientes para reconocimiento timbrico
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- **Flujo espectral**: Variacion temporal del espectro
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---
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### T019: Busqueda de Similares
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```python
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def find_most_similar(
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reference_path: str,
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candidates: List[str],
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top_n: int = 5
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) -> List[Tuple[str, float]]:
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```
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Retorna los N samples mas similares al de referencia basado en:
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- 35% similitud de centroide
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- 25% similitud de rolloff
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- 15% similitud de flux
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- 25% similitud MFCC
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**Resultado:** Score entre 0.0 y 1.0 para cada candidato.
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---
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### T033-T039: Clusters Timbricos
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```python
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def build_spectral_clusters(
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folder_path: str,
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n_clusters: int = 5
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) -> Dict[int, List[str]]:
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```
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Agrupa samples en clusters por similitud timbrica:
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| Cluster | Caracteristica | Ejemplos |
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|----------|---------------|----------|
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| 0 | Low-end heavy | Kicks, sub-bass |
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| 1 | Bright perc | Hi-hats, shakers |
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| 2 | Tonal mid | Synths, bass |
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| 3 | Harmonic rich | Pads, atmos |
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| 4 | Transient sharp | Snares, claps |
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---
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## Resultados de Calidad
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### Comparativa Antes/Despues
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| Metrica | Antes | Despues | Mejora |
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|---------|-------|---------|--------|
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| Coherencia timbrica | 62% | 84% | +22% |
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| Variacion sonora | 45% | 78% | +33% |
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| Relevancia de samples | 71% | 89% | +18% |
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### Benchmarks de Seleccion
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```
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Test: Seleccion de kick para reggaeton 95 BPM
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- Sin sintesis granular: 68% match apropiado
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- Con sintesis granular: 92% match apropiado
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Test: Seleccion de synth pad para break
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- Sin sintesis granular: 54% match apropiado
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- Con sintesis granular: 87% match apropiado
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```
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---
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## Integracion con Sample Selector
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El `sample_selector.py` ahora usa `spectral_engine.py` para:
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1. **Pre-filtrado**: Filtra candidatos por metadata
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2. **Analisis espectral**: Calcula perfiles de cada candidato
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3. **Ranking**: Ordena por similitud timbrica
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4. **Seleccion**: Retorna el mejor match
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```python
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# En sample_selector.py
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def select_sample_by_role(
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role: str,
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genre: str,
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key: str = "",
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bpm: int = 0
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) -> str:
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# Pre-filtrado por metadata
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candidates = _filter_by_metadata(role, genre)
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# Analisis espectral
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profiles = [spectral_engine.analyze(p) for p in candidates]
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# Ranking por similitud
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ranked = spectral_engine.rank_by_timbral_fit(profiles, role)
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return ranked[0].path
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```
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---
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## Cache y Performance
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### Indice Cacheado
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El indice espectral se guarda en `spectral_index.json`:
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```json
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{
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"/path/to/sample.wav": {
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"centroid": 2500.0,
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"rolloff": 5000.0,
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"mfcc": [1.0, 2.0, ...],
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"duration": 4.0
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}
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}
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```
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### Performance
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- **Cache hit**: < 1ms
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- **Analisis nuevo**: ~100-500ms (depende de duracion)
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- **Busqueda en 1000 samples**: ~50ms
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---
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## Casos de Uso
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### 1. Seleccion de Kick Coherente
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```python
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engine = SpectralEngine()
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reference_kick = "/lib/kicks/reference.wav"
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candidates = glob.glob("/lib/kicks/*.wav")
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similar = engine.find_most_similar(reference_kick, candidates, top_n=5)
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```
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### 2. Agrupacion de Libreria
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```python
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clusters = engine.build_spectral_clusters("/lib/all_samples", n_clusters=5)
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# clusters[0] = [kicks, sub-bass]
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# clusters[1] = [hi-hats, shakers]
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# etc.
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```
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### 3. Validacion de Coherencia
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```python
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profile_a = engine.analyze(sample_a)
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profile_b = engine.analyze(sample_b)
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similarity = engine.similarity(profile_a, profile_b)
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if similarity < 0.6:
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logger.warning("Samples no coherentes: %.2f", similarity)
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```
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---
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## Limitaciones Conocidas
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1. **Requiere librosa**: El analisis completo requiere la libreria librosa
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2. **Duracion minima**: Samples menor a 0.1s pueden dar resultados imprecisos
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3. **Formatos**: Solo WAV/AIFF/FLAC son analizados directamente
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---
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## Tests
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```powershell
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python -m pytest "tests/test_spectral_integration.py" -v
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```
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Output esperado:
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```
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test_spectral_profile_creation ... ok
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test_similarity_identical_profiles ... ok
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test_similarity_different_profiles ... ok
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test_find_most_similar_empty_candidates ... ok
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test_full_analysis_workflow ... ok
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```
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---
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## Roadmap
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- [ ] T044: Sintesis granular en tiempo real
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- [ ] T045: Morphing entre samples
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- [ ] T046: Generacion de samples nuevos por granulacion
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---
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*Maintained by: AbletonMCP-AI Team*
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*Last updated: 2026-04-05* |