05_26_MACHINE_LEARNING - Implementation Plan¶

Status: 🔴 PLANNING COMPLETE - Ready for Phase 1 implementation Created: 2025-10-15 Target Completion: Q4 2025

🎯 Overview¶

This document outlines the complete implementation plan for AudioLab's Machine Learning subsystem, covering all 10 TAREAS from foundation (ML Framework) to advanced features (Audio Restoration).

📊 Implementation Phases¶

PHASE 1: Foundation (Q1 2025 - 10 weeks)¶

Goal: Build core ML infrastructure

TAREA 00: ML Framework ⚡ CRITICAL¶

Week 1-2: Core interfaces (IModelLoader, IInferenceEngine, Tensor)
Week 3: ONNX Runtime backend (CPU, CUDA, DirectML)
Week 4: TensorFlow Lite backend (mobile, CoreML)
Week 5: LibTorch backend (PyTorch C++)
Week 6: Quantization & optimization pipeline

Deliverables: - [x] Architecture design - [ ] ml_framework library with 3 backends - [ ] Model quantization tools (FP32 → FP16 → INT8) - [ ] Comprehensive unit tests - [ ] Performance benchmarks

Success Criteria: - ✓ Load ONNX/TFLite/TorchScript models - ✓ Run inference on CPU/GPU - ✓ < 5ms latency for small models - ✓ INT8 quantization working

PHASE 2: Core ML Features (Q2 2025 - 12 weeks)¶

TAREA 05: Noise Reduction 🔥 HIGH PRIORITY¶

Week 1-2: RNNoise integration
Week 3-4: Real-time denoising pipeline
Week 5-6: Speech enhancement (DeepFilterNet)

Use Cases: Podcast cleanup, voice-over cleaning

TAREA 03: Classification 🔥 HIGH PRIORITY¶

Week 7-8: Feature extraction (MFCC, mel-spec)
Week 9-10: CNN classifier integration (VGGish, YAMNet)
Week 11-12: Real-time classification with sliding window

Use Cases: Instrument detection, genre classification

TAREA 07: Anomaly Detection 🟡 MEDIUM¶

Week 13-14: Autoencoder-based detection
Week 15: Real-time monitoring

Use Cases: Live performance monitoring, quality check

Success Criteria: - ✓ Real-time noise reduction (PESQ > 3.5) - ✓ Accurate classification (>90% accuracy on test set) - ✓ Anomaly detection working in real-time

PHASE 3: Advanced Features (Q3 2025 - 14 weeks)¶

TAREA 04: Source Separation 🔥 HIGH PRIORITY¶

Week 1-3: Spleeter 4-stem integration
Week 4-6: Demucs hybrid separation
Week 7: Real-time stem extraction

Use Cases: Remix preparation, vocal removal

TAREA 01: Audio Generation 🔥 HIGH PRIORITY¶

Week 8-9: DDSP implementation
Week 10-11: Timbre transfer
Week 12-13: WaveNet/NSynth integration
Week 14: GANSynth integration

Use Cases: Neural synthesis, AI-powered instruments

TAREA 02: Parameter Optimization 🔥 HIGH PRIORITY¶

Week 15-16: Genetic algorithm optimizer
Week 17: Differentiable DSP
Week 18: Multi-objective optimization

Use Cases: Auto-tune compressor, EQ matching

Success Criteria: - ✓ 4-stem separation (SDR > 6 dB vocals) - ✓ DDSP timbre transfer working - ✓ Auto-parameter tuning functional

TAREA 06: Preset Generation 🟡 MEDIUM¶

Week 1-2: Audio-to-preset mapping
Week 3-4: Preset recommendation engine
Week 5: Preset interpolation

Use Cases: "Make it sound like X"

TAREA 08: Personalization 🟡 MEDIUM¶

Week 6-7: User interaction tracking
Week 8: Collaborative filtering
Week 9: Context-aware recommendations
Week 10: Reinforcement learning integration

Use Cases: Adaptive processing, smart recommendations

TAREA 09: Audio Restoration 🟡 MEDIUM¶

Week 11-12: Declipping network
Week 13: Bandwidth extension
Week 14: Historical restoration

Use Cases: Vinyl restoration, low-bitrate upsampling

Success Criteria: - ✓ Preset generation from reference audio - ✓ Personalization improving over time - ✓ Restoration quality acceptable (PESQ > 3.5)

🏗️ Technical Architecture¶

Dependency Graph¶

Level 0 (Foundation):
  ├─ 05_26_00_ml_framework ← ALL other TAREAS depend on this

Level 1 (Core Features):
  ├─ 05_26_03_classification
  ├─ 05_26_05_noise_reduction
  └─ 05_26_07_anomaly_detection

Level 2 (Advanced Features):
  ├─ 05_26_04_source_separation
  ├─ 05_26_01_audio_generation
  └─ 05_26_02_parameter_optimization
      └─ depends on: 01 (DDSP for differentiable DSP)

Level 3 (Intelligence):
  ├─ 05_26_06_preset_generation
  │   └─ depends on: 02 (parameter optimization), 03 (classification)
  ├─ 05_26_08_personalization
  │   └─ depends on: 03 (classification), 06 (preset generation)
  └─ 05_26_09_audio_restoration
      └─ depends on: 05 (noise reduction)

Integration Points¶

External Dependencies:
  ├─ ONNX Runtime (Microsoft)
  ├─ TensorFlow Lite (Google)
  ├─ LibTorch (PyTorch C++)
  └─ OpenVINO (Intel, optional)

AudioLab Internal Dependencies:
  ├─ 05_04_DSP_PROCESSING     (Audio primitives, FFT)
  ├─ 05_11_GRAPH_SYSTEM       (ML nodes in graph)
  ├─ 05_14_PRESET_SYSTEM      (Preset integration)
  ├─ 05_16_PERFORMANCE_VARIANTS (SIMD optimization)
  └─ 05_25_AI_ORCHESTRATOR    (Model orchestration)

📦 Deliverables by Phase¶

Phase 1 Deliverables¶

libml_framework.a - Core ML library
Model quantization CLI tool
Performance benchmark suite
Documentation (API reference)

Phase 2 Deliverables¶

libml_noise_reduction.a
libml_classification.a
libml_anomaly_detection.a
Pre-trained models (RNNoise, VGGish, YAMNet)
Real-time processing examples

Phase 3 Deliverables¶

libml_source_separation.a (Spleeter, Demucs)
libml_audio_generation.a (DDSP, WaveNet, GANs)
libml_parameter_optimization.a
Pre-trained models (Spleeter 4-stem, DDSP violin/flute)

Phase 4 Deliverables¶

libml_preset_generation.a
libml_personalization.a
libml_audio_restoration.a
Complete ML subsystem integration

🧪 Testing Strategy¶

Unit Tests (Per TAREA)¶

Model loading correctness
Inference output validation
Performance regression tests
Memory leak detection

Integration Tests¶

Multi-backend comparison (ONNX vs TFLite vs Torch)
Real-time processing pipeline
Graph system integration

Performance Tests¶

Latency benchmarks (p50, p95, p99)
Throughput testing
CPU/GPU utilization profiling
Memory usage under load

Quality Tests¶

Audio quality metrics (PESQ, POLQA, SDR)
Perceptual listening tests
A/B comparison with reference implementations

📊 Success Metrics¶

Performance Metrics¶

Metric	Target	Measured
Model Load Time	< 500ms	TBD
Small Model Latency	< 5ms	TBD
Medium Model Latency	< 20ms	TBD
CPU Usage (real-time)	< 15%	TBD
Memory per Model	< 100MB	TBD

Quality Metrics¶

Metric	Target	Measured
Noise Reduction (PESQ)	> 3.5	TBD
Classification Accuracy	> 90%	TBD
Source Separation (SDR)	> 6 dB	TBD
Restoration Quality	> 3.5 PESQ	TBD

User Experience Metrics¶

Metric	Target	Measured
Preset Recommendation Acceptance	> 75%	TBD
Personalization Improvement	+20% over time	TBD
Feature Usage	> 50% users try ML features	TBD

🔧 Development Tools¶

Build System¶

# CMakeLists.txt for ML subsystem
find_package(onnxruntime REQUIRED)
find_package(TensorFlowLite REQUIRED)
find_package(Torch REQUIRED)

add_subdirectory(05_26_00_ml_framework)
add_subdirectory(05_26_01_audio_generation)
# ... etc

Model Management¶

Training: Python (PyTorch, TensorFlow)
Conversion: ONNX export, TFLite conversion
Optimization: ONNX Optimizer, TFLite Converter
Deployment: Model registry, versioning

CI/CD Pipeline¶

Automated testing on push
Performance benchmarks on PR
Model validation before merge
Nightly builds with latest models

📚 Documentation Requirements¶

Per TAREA Documentation¶

System-Wide Documentation¶

⚠️ Risk Mitigation¶

Technical Risks¶

Risk	Impact	Mitigation
Model latency too high	High	Quantization, model pruning, GPU acceleration
Accuracy below target	High	Larger models, more training data, ensemble methods
Memory usage excessive	Medium	Model compression, streaming inference
GPU availability	Medium	CPU fallback, DirectML, Metal support

Project Risks¶

Risk	Impact	Mitigation
Phase 1 delay	Critical	Prioritize core framework, parallel development
Model licensing issues	High	Use only open-source/permissive models
Hardware compatibility	Medium	Multi-backend support, extensive testing

🚀 Next Steps¶

Immediate Actions (This Week)¶

Set up development environment
Install ONNX Runtime, TFLite, LibTorch
Create CMake build configuration
Implement core IModelLoader interface

Week 2-3¶

Implement Tensor abstraction
Create ONNX backend
Write first unit tests

Month 1 Goal¶

Complete TAREA 00 (ML Framework)
Successfully load and run first ONNX model
Benchmarks showing < 5ms latency

📞 Contact & Support¶

Primary Developer: TBD ML Research Lead: TBD Integration Support: AudioLab Core Team

Last Updated: 2025-10-15 Version: 1.0 Status: 🔴 Planning Complete - Ready for Implementation