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05_26_MACHINE_LEARNING - Planning Complete ✅

Date: 2025-10-15 Status: 🟢 PLANNING COMPLETE - Ready for implementation

📊 Summary

The complete planning phase for AudioLab's Machine Learning subsystem has been completed. All 10 TAREAS have been architecturally designed with detailed implementation plans.

✅ Deliverables Created

📁 Folder Structure (100% Complete)

05_26_MACHINE_LEARNING/
├── 05_26_00_ml_framework/          ✅ Core ML infrastructure
├── 05_26_01_audio_generation/      ✅ Neural synthesis
├── 05_26_02_parameter_optimization/✅ Auto DSP tuning
├── 05_26_03_classification/        ✅ Audio classification
├── 05_26_04_source_separation/     ✅ Stem extraction
├── 05_26_05_noise_reduction/       ✅ ML denoising
├── 05_26_06_preset_generation/     ✅ Preset AI
├── 05_26_07_anomaly_detection/     ✅ Quality monitoring
├── 05_26_08_personalization/       ✅ Adaptive processing
└── 05_26_09_audio_restoration/     ✅ Audio repair

Each TAREA contains: - ✅ include/ - Header files directory - ✅ src/ - Implementation directory - ✅ tests/ - Unit tests directory - ✅ examples/ - Usage examples directory - ✅ models/ - Pre-trained models directory - ✅ data/ - Sample datasets directory - ✅ README.md - Complete documentation

📖 Documentation (100% Complete)

Main Documentation

  • ✅ README.md - Subsystem overview, architecture, roadmap
  • ✅ IMPLEMENTATION_PLAN.md - 4-phase implementation plan with timelines
  • ✅ INTEGRATION_GUIDE.md - Integration with other subsystems
  • ✅ PLANNING_COMPLETE.md - This document

TAREA-Specific Documentation (10/10 Complete)

  1. ✅ TAREA 00: ML Framework - Core infrastructure (ONNX, TFLite, LibTorch)
  2. ✅ TAREA 01: Audio Generation - WaveNet, DDSP, NSynth, GANs, Diffusion
  3. ✅ TAREA 02: Parameter Optimization - Genetic algorithms, Differentiable DSP
  4. ✅ TAREA 03: Classification - VGGish, YAMNet, instrument/genre detection
  5. ✅ TAREA 04: Source Separation - Spleeter, Demucs, 4-stem extraction
  6. ✅ TAREA 05: Noise Reduction - RNNoise, DeepFilterNet, speech enhancement
  7. ✅ TAREA 06: Preset Generation - Audio-to-preset, recommendations
  8. ✅ TAREA 07: Anomaly Detection - Autoencoder, quality monitoring
  9. ✅ TAREA 08: Personalization - User models, adaptive processing
  10. ✅ TAREA 09: Audio Restoration - Declipping, bandwidth extension

🎯 Implementation Priorities

🔥 Critical Path (Must-Have)

TAREA 00: ML Framework - Foundation for everything - ONNX Runtime, TFLite, LibTorch integration - Model loading, inference, quantization - CPU/GPU acceleration

🔥 High Priority (Phase 2)

TAREA 05: Noise Reduction - High user demand - RNNoise integration - Real-time speech enhancement

TAREA 03: Classification - Enables intelligent routing - Instrument/genre detection - Event classification

TAREA 04: Source Separation - Key production feature - 4-stem extraction (vocals, drums, bass, other) - Real-time separation

🟡 Medium Priority (Phase 3-4)

  • TAREA 01: Audio Generation (neural synthesis)
  • TAREA 02: Parameter Optimization (auto-tuning)
  • TAREA 06: Preset Generation (AI presets)
  • TAREA 07: Anomaly Detection (quality monitoring)
  • TAREA 08: Personalization (adaptive processing)
  • TAREA 09: Audio Restoration (audio repair)

📈 Implementation Timeline

Phase 1: Foundation (Q1 2025 - 10 weeks)

Goal: Build ML Framework core - Week 1-6: TAREA 00 implementation - Week 7-10: Testing, optimization, documentation

Deliverable: Working ML inference engine with 3 backends

Phase 2: Core Features (Q2 2025 - 12 weeks)

Goal: Production-ready ML features - TAREA 05: Noise Reduction - TAREA 03: Classification - TAREA 07: Anomaly Detection

Deliverable: 3 production-ready ML effects

Phase 3: Advanced Features (Q3 2025 - 14 weeks)

Goal: Advanced ML capabilities - TAREA 04: Source Separation - TAREA 01: Audio Generation - TAREA 02: Parameter Optimization

Deliverable: Advanced ML processing suite

Phase 4: Intelligence (Q4 2025 - 10 weeks)

Goal: AI-powered intelligence - TAREA 06: Preset Generation - TAREA 08: Personalization - TAREA 09: Audio Restoration

Deliverable: Complete ML subsystem

🔧 Technical Architecture

Multi-Backend ML Framework

Application Layer
     ↓
ML Framework (05_26_00)
     ↓
┌─────────┬──────────┬─────────┐
│  ONNX   │  TFLite  │ LibTorch│
│ Runtime │          │ (PyTorch)│
└─────────┴──────────┴─────────┘
     ↓
┌─────────┬──────────┬─────────┐
│   CPU   │   GPU    │   NPU   │
│  (SIMD) │(CUDA/DML)│(CoreML) │
└─────────┴──────────┴─────────┘

Integration with AudioLab

05_26_ML ←→ 05_11_GRAPH_SYSTEM (ML nodes in audio graph)
05_26_ML ←→ 05_04_DSP (Feature extraction, post-processing)
05_26_ML ←→ 05_14_PRESET_SYSTEM (AI presets)
05_26_ML ←→ 05_16_PERFORMANCE_VARIANTS (SIMD optimization)
05_26_ML ←→ 05_25_AI_ORCHESTRATOR (Model orchestration)

📊 Success Metrics

Performance Targets

Metric Target Status
Model Load Time < 500ms 🔴 Not started
Inference Latency (small) < 5ms 🔴 Not started
Inference Latency (medium) < 20ms 🔴 Not started
CPU Usage (real-time) < 15% 🔴 Not started
Memory per Model < 100MB 🔴 Not started

Quality Targets

Feature Target Status
Noise Reduction (PESQ) > 3.5 🔴 Not started
Classification Accuracy > 90% 🔴 Not started
Source Separation (SDR) > 6 dB 🔴 Not started
Restoration Quality > 3.5 PESQ 🔴 Not started

📚 Key Technologies

ML Frameworks

  • ONNX Runtime - Cross-platform inference (Microsoft)
  • TensorFlow Lite - Mobile/embedded deployment (Google)
  • LibTorch - PyTorch C++ API (Meta)
  • OpenVINO - Intel CPU/GPU optimization (optional)

Pre-Trained Models

  • RNNoise - Real-time noise suppression
  • Spleeter - 4-stem source separation (Deezer)
  • Demucs - Advanced source separation (Meta)
  • VGGish - Audio classification (Google)
  • YAMNet - AudioSet classification (Google)
  • DDSP - Differentiable DSP (Google Magenta)

Audio ML Libraries

  • librosa - Feature extraction
  • essentia - Audio analysis
  • nnAudio - Differentiable audio processing

🚀 Next Steps

Immediate Actions (Week 1)

  1. ✅ Planning complete
  2. ⏳ Set up development environment
  3. ⏳ Install ONNX Runtime, TFLite, LibTorch
  4. ⏳ Create CMake build configuration
  5. ⏳ Begin TAREA 00 implementation

Week 2-3

  1. ⏳ Implement IModelLoader interface
  2. ⏳ Implement Tensor abstraction
  3. ⏳ Create ONNX backend
  4. ⏳ Write first unit tests

Month 1 Milestone

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

🎓 Learning Resources

  1. WaveNet (van den Oord et al., 2016) - Audio synthesis
  2. DDSP (Engel et al., 2020) - Differentiable DSP
  3. Spleeter (Deezer, 2019) - Source separation
  4. Demucs (Défossez et al., 2021) - Hybrid separation
  5. RNNoise (Valin, 2018) - Real-time denoising

Tutorials

  • ONNX Runtime C++ API
  • TensorFlow Lite for C++
  • LibTorch (PyTorch C++) tutorials
  • Audio ML best practices

📞 Contact

ML Architecture: Designed and planned Implementation Team: TBD Integration Support: AudioLab Core Team

✅ Planning Phase Checklist

  • ✅ Architecture design complete
  • ✅ 10 TAREAS defined
  • ✅ Folder structure created
  • ✅ README.md for all TAREAS
  • ✅ Implementation plan documented
  • ✅ Integration guide created
  • ✅ Priorities established
  • ✅ Timeline defined
  • ✅ Success metrics specified
  • ✅ Technologies selected
  • ✅ Resources identified

🎉 Ready for Implementation!

The ML subsystem planning is 100% complete. All architectural decisions have been made, documentation is in place, and the implementation roadmap is clear.

Status: 🟢 READY TO CODE

Planning Completed: 2025-10-15 Version: 1.0 Next Phase: Implementation begins with TAREA 00 (ML Framework)