05_26_MACHINE_LEARNING - Executive Planning Summary¶
Date: 2025-10-15 Status: 🟢 PLANNING COMPLETE Time Investment: ~3 hours of comprehensive planning Ready For: Implementation Phase 1 (ML Framework)
📊 What Was Accomplished¶
✅ Complete Architectural Planning (100%)¶
10 TAREAS Planned & Documented:
| TAREA | Name | Priority | Complexity | Est. Time |
|---|---|---|---|---|
| 00 | ML Framework | 🔥 CRITICAL | High | 6 weeks |
| 01 | Audio Generation | 🔥 HIGH | Very High | 10 weeks |
| 02 | Parameter Optimization | 🔥 HIGH | High | 6 weeks |
| 03 | Classification | 🔥 HIGH | Medium | 6 weeks |
| 04 | Source Separation | 🔥 HIGH | High | 7 weeks |
| 05 | Noise Reduction | 🔥 CRITICAL | Medium | 6 weeks |
| 06 | Preset Generation | 🟡 MEDIUM | Medium | 5 weeks |
| 07 | Anomaly Detection | 🟡 MEDIUM | Medium | 3 weeks |
| 08 | Personalization | 🟡 MEDIUM | High | 4 weeks |
| 09 | Audio Restoration | 🟡 MEDIUM | High | 4 weeks |
Total Estimated Implementation Time: ~57 weeks (~1 year with parallel development)
📁 Deliverables Created¶
Documentation (14 Markdown Files)¶
✅ README.md (9.5 KB) - Main subsystem overview
✅ IMPLEMENTATION_PLAN.md (10.0 KB) - 4-phase roadmap
✅ INTEGRATION_GUIDE.md (14.0 KB) - Integration patterns
✅ PLANNING_COMPLETE.md (8.3 KB) - Planning status
✅ ML_PLANNING_SUMMARY.md (this file) - Executive summary
✅ STRUCTURE.txt (4.2 KB) - Visual folder tree
✅ 05_26_00_ml_framework/README.md (15.8 KB)
✅ 05_26_01_audio_generation/README.md (12.4 KB)
✅ 05_26_02_parameter_optimization/README.md (6.2 KB)
✅ 05_26_03_classification/README.md (2.8 KB)
✅ 05_26_04_source_separation/README.md (3.1 KB)
✅ 05_26_05_noise_reduction/README.md (3.5 KB)
✅ 05_26_06_preset_generation/README.md (2.9 KB)
✅ 05_26_07_anomaly_detection/README.md (3.2 KB)
✅ 05_26_08_personalization/README.md (4.1 KB)
✅ 05_26_09_audio_restoration/README.md (3.9 KB)
Total Documentation: ~103 KB of comprehensive planning docs
Folder Structure (70 Directories)¶
🎯 Key Technical Decisions¶
1. Multi-Backend ML Framework¶
Decision: Support 3 inference backends - ONNX Runtime - Primary (cross-platform, production) - TensorFlow Lite - Mobile/embedded - LibTorch - Advanced PyTorch models
Rationale: Maximum flexibility, platform coverage, and performance
2. Model Format Strategy¶
Decision: Standardize on ONNX as primary format - Convert all models to ONNX - Use TFLite for mobile-specific deployments - Use TorchScript for PyTorch-native models
Rationale: ONNX provides best cross-platform compatibility
3. Hardware Acceleration¶
Decision: Multi-tier acceleration strategy - CPU: SIMD optimization, multi-threading - GPU: CUDA (NVIDIA), DirectML (Windows), Metal (Apple) - NPU: CoreML (Apple Neural Engine), OpenVINO (Intel VPU)
Rationale: Maximize performance on available hardware
4. Real-Time Processing¶
Decision: < 20ms latency target for real-time effects - Small models (< 1MB): < 5ms - Medium models (1-10MB): < 10ms - Large models (> 10MB): < 20ms
Rationale: Professional audio requires low-latency processing
5. Model Optimization¶
Decision: Aggressive quantization pipeline - FP32 → FP16 (2x smaller, minimal quality loss) - FP32 → INT8 (4x smaller, acceptable quality loss) - Dynamic quantization for inference-only models
Rationale: Reduce memory footprint and improve inference speed
📈 Implementation Roadmap¶
Phase 1: Foundation (Q1 2025 - 10 weeks)¶
TAREA 00: ML Framework - Build core inference engine - Integrate ONNX/TFLite/LibTorch - Implement quantization pipeline
Goal: Load and run first ML model with < 5ms latency
Phase 2: Core Features (Q2 2025 - 12 weeks)¶
TAREA 05, 03, 07 - Noise Reduction (RNNoise) - Classification (VGGish, YAMNet) - Anomaly Detection (Autoencoder)
Goal: 3 production-ready ML effects
Phase 3: Advanced Features (Q3 2025 - 14 weeks)¶
TAREA 04, 01, 02 - Source Separation (Spleeter, Demucs) - Audio Generation (DDSP, WaveNet) - Parameter Optimization (Genetic algorithms)
Goal: Advanced ML processing suite
Phase 4: Intelligence (Q4 2025 - 10 weeks)¶
TAREA 06, 08, 09 - Preset Generation (Audio-to-preset) - Personalization (User models) - Audio Restoration (Declipping, bandwidth extension)
Goal: Complete ML subsystem with AI intelligence
🔗 Integration Strategy¶
With Existing Subsystems¶
05_11_GRAPH_SYSTEM - ML nodes in audio graph - Dynamic routing based on classification - Real-time ML processing
05_04_DSP_PROCESSING - Feature extraction (FFT, mel-spectrograms) - Post-processing (resampling, filtering) - Hybrid ML + DSP processing
05_14_PRESET_SYSTEM - AI-generated presets - Preset recommendation engine - Audio-to-preset mapping
05_16_PERFORMANCE_VARIANTS - SIMD-optimized tensor operations - GPU-accelerated inference - Multi-threaded model execution
05_25_AI_ORCHESTRATOR - Multi-model coordination - Resource management - Training pipeline integration
📊 Success Criteria¶
Performance Metrics¶
| Metric | Target | Critical Path |
|---|---|---|
| Model Load Time | < 500ms | Phase 1 |
| Inference Latency (small) | < 5ms | Phase 1 |
| Inference Latency (medium) | < 20ms | Phase 2 |
| CPU Usage (real-time) | < 15% | Phase 2 |
| Memory per Model | < 100MB | Phase 1 |
Quality Metrics¶
| Feature | Target | Phase |
|---|---|---|
| Noise Reduction (PESQ) | > 3.5 | Phase 2 |
| Classification Accuracy | > 90% | Phase 2 |
| Source Separation (SDR) | > 6 dB | Phase 3 |
| Audio Generation Quality | > 4.0 MOS | Phase 3 |
| Restoration Quality (PESQ) | > 3.5 | Phase 4 |
User Experience Metrics¶
| Metric | Target | Phase |
|---|---|---|
| Preset Recommendation Acceptance | > 75% | Phase 4 |
| Personalization Improvement | +20% over time | Phase 4 |
| Feature Discovery | > 50% users try ML | All Phases |
🚀 Immediate Next Steps¶
Week 1 (This Week)¶
- ✅ Planning complete
- ⏳ Set up development environment
- ⏳ Install ONNX Runtime SDK
- ⏳ Install TensorFlow Lite C++ API
- ⏳ Install LibTorch (PyTorch C++)
- ⏳ Create initial CMake configuration
Week 2-3¶
- ⏳ Implement
IModelLoaderinterface - ⏳ Implement
Tensorabstraction - ⏳ Create ONNX backend (CPU-only first)
- ⏳ Write first unit tests
- ⏳ Load and run first ONNX model
Month 1 Milestone¶
- ⏳ Complete TAREA 00 implementation
- ⏳ Successfully run inference on test model
- ⏳ Achieve < 5ms latency for small models
- ⏳ Pass all unit tests
- ⏳ Publish API documentation
🎓 Key Technologies & Models¶
ML Frameworks¶
- ONNX Runtime 1.16+ (Microsoft)
- TensorFlow Lite 2.14+ (Google)
- LibTorch 2.1+ (Meta/PyTorch)
- OpenVINO 2024.0+ (Intel, optional)
Pre-Trained Models (To Be Integrated)¶
- RNNoise - Real-time noise suppression
- Spleeter - 4-stem source separation (Deezer)
- Demucs v3 - Advanced hybrid separation (Meta)
- VGGish - Audio embedding/classification (Google)
- YAMNet - AudioSet classification, 521 classes (Google)
- DDSP - Differentiable DSP models (Google Magenta)
- WaveNet - Neural audio synthesis (DeepMind)
- NSynth - Neural synthesizer (Google Magenta)
Audio ML Libraries¶
- librosa - Feature extraction (Python)
- essentia - Audio analysis (C++ with Python bindings)
- nnAudio - Differentiable audio transforms
⚠️ Known Challenges & Mitigations¶
Challenge 1: Real-Time Latency¶
Risk: ML models too slow for real-time audio (< 20ms target)
Mitigation: - Aggressive model quantization (INT8) - GPU acceleration where available - Streaming inference for long sequences - Model pruning and distillation
Challenge 2: Memory Footprint¶
Risk: Multiple large models exceed memory limits
Mitigation: - Model compression and quantization - Lazy loading (load on demand) - Model sharing between instances - Memory pooling
Challenge 3: Cross-Platform Compatibility¶
Risk: Different inference performance on different platforms
Mitigation: - Multi-backend strategy (ONNX, TFLite, LibTorch) - Platform-specific optimization - Extensive cross-platform testing - Fallback to CPU if GPU unavailable
Challenge 4: Model Quality vs Performance¶
Risk: Quality degradation with optimization
Mitigation: - Perceptual quality metrics (PESQ, POLQA) - A/B testing with reference implementations - User feedback collection - Multiple quality presets (fast/balanced/high-quality)
💡 Innovation Highlights¶
1. Hybrid ML + DSP Architecture¶
Innovation: Combine ML intelligence with classical DSP efficiency - DDSP: Learn interpretable parameters, synthesize with DSP - Classification-driven DSP routing - ML parameter optimization for DSP effects
2. Multi-Backend Inference¶
Innovation: Seamless switching between inference backends - Same model, multiple backends (ONNX/TFLite/Torch) - Automatic backend selection based on platform - Runtime performance profiling
3. Personalized Audio Processing¶
Innovation: Adaptive effects that learn user preferences - Collaborative filtering for preset recommendations - Context-aware processing (genre, mood, time) - Reinforcement learning for continuous improvement
4. Real-Time Source Separation¶
Innovation: Live stem extraction during performance - Optimized Spleeter/Demucs for real-time use - GPU-accelerated separation - Adaptive quality based on CPU budget
📞 Team & Resources¶
Roles Needed¶
- ML Engineer (Lead) - Framework implementation
- Audio DSP Engineer - Hybrid ML+DSP integration
- Backend Engineer - Model deployment, orchestration
- QA Engineer - Testing, benchmarking, validation
External Resources¶
- ONNX Runtime documentation
- TensorFlow Lite C++ guide
- LibTorch tutorials
- Pre-trained model repositories (Hugging Face, Google Magenta)
🎉 Conclusion¶
The 05_26_MACHINE_LEARNING subsystem planning is 100% complete with:
✅ 10 TAREAS fully architected ✅ 14 documentation files (~103 KB) ✅ 70 directories created ✅ 4-phase roadmap with timelines ✅ Integration strategy defined ✅ Success metrics established ✅ Technology stack selected
Next Step: Begin Phase 1 implementation (TAREA 00: ML Framework)
Prepared By: AudioLab Architecture Team Date: 2025-10-15 Status: 🟢 READY FOR IMPLEMENTATION Est. Completion: Q4 2025 (full subsystem)