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05_26_MACHINE_LEARNING - Machine Learning for Audio

🎯 Purpose

Machine Learning subsystem for intelligent audio processing, generation, optimization, and restoration using modern ML/AI techniques.

Status: 🔴 PLANNING - Architecture defined, implementation pending

📋 Overview

This subsystem integrates machine learning and artificial intelligence into AudioLab's audio processing pipeline, enabling:

  • Audio Generation: Neural synthesis, style transfer, generative models
  • Intelligent Optimization: Automatic parameter tuning, DSP optimization
  • Audio Analysis: Classification, source separation, anomaly detection
  • Audio Restoration: Noise reduction, denoising, artifact removal
  • Personalization: Adaptive processing based on user preferences

🗂️ Architecture

10 TAREAS (00-09)

05_26_MACHINE_LEARNING/
├── 05_26_00_ml_framework/          # Core ML infrastructure
├── 05_26_01_audio_generation/      # Neural audio synthesis
├── 05_26_02_parameter_optimization/# Automatic DSP tuning
├── 05_26_03_classification/        # Audio classification
├── 05_26_04_source_separation/     # Multi-source separation
├── 05_26_05_noise_reduction/       # ML-based denoising
├── 05_26_06_preset_generation/     # Intelligent preset creation
├── 05_26_07_anomaly_detection/     # Audio quality monitoring
├── 05_26_08_personalization/       # Adaptive user models
└── 05_26_09_audio_restoration/     # ML-based restoration

🎓 TAREA Breakdown

TAREA 00: ML Framework 🔴 PLANNING

Core ML infrastructure, model management, inference engine

  • Model loader/serializer (ONNX, TensorFlow Lite, custom formats)
  • Inference engine abstraction (CPU, GPU, NPU)
  • Training pipeline integration
  • Model versioning and deployment

Key Technologies: - ONNX Runtime for cross-platform inference - TensorFlow Lite for mobile deployment - LibTorch (PyTorch C++) for advanced models - Custom quantization and optimization

TAREA 01: Audio Generation 🔴 PLANNING

Neural synthesis, style transfer, generative models

  • Neural oscillators (WaveNet, SampleRNN)
  • Timbre transfer (NSynth, DDSP)
  • Generative models (GANs, VAEs, diffusion models)
  • Conditional synthesis

Use Cases: - AI-powered synthesizers - Voice cloning for vocoders - Texture generation for sound design - Neural drum synthesis

TAREA 02: Parameter Optimization 🔴 PLANNING

Automatic DSP parameter tuning using ML

  • Gradient-free optimization (genetic algorithms, particle swarm)
  • Differentiable DSP for gradient-based optimization
  • Preset interpolation and morphing
  • Multi-objective optimization (sound quality + CPU usage)

Use Cases: - Auto-tune compressor attack/release - Optimal EQ curve fitting - Reverb parameter matching - Multi-band dynamics optimization

TAREA 03: Classification 🔴 PLANNING

Audio event detection, instrument recognition, genre classification

  • Feature extraction (MFCC, mel-spectrograms, embeddings)
  • CNN-based classifiers
  • RNN/LSTM for temporal patterns
  • Transfer learning (VGGish, PANN, YAMNet)

Use Cases: - Automatic instrument detection - Genre/style classification - Transient detection (kick, snare) - Speech/music discrimination

TAREA 04: Source Separation 🔴 PLANNING

Multi-track separation, stem extraction

  • Vocal/instrumental separation (Spleeter, Demucs)
  • Multi-stem separation (drums, bass, vocals, other)
  • Time-frequency masking
  • End-to-end waveform separation

Use Cases: - Karaoke vocal removal - Remix/mashup preparation - Mixing assistance (isolate elements) - Transcription preprocessing

TAREA 05: Noise Reduction 🔴 PLANNING

ML-based denoising, artifact removal

  • Spectral denoising (Wiener filtering + ML)
  • Waveform-to-waveform denoising (WaveUNet, SEGAN)
  • Speech enhancement (background noise, reverb removal)
  • Adaptive noise profiling

Use Cases: - Podcast cleanup - Field recording restoration - Live broadcast denoising - Voice-over cleaning

TAREA 06: Preset Generation 🔴 PLANNING

Intelligent preset creation and recommendation

  • Audio-to-preset mapping (inverse synthesis)
  • Preset recommendation based on audio content
  • Style transfer for presets
  • User preference learning

Use Cases: - "Make it sound like X" feature - Genre-aware preset suggestions - Automatic preset generation from reference audio - User profile-based presets

TAREA 07: Anomaly Detection 🔴 PLANNING

Audio quality monitoring, glitch detection

  • Autoencoder-based anomaly detection
  • Statistical outlier detection
  • Real-time quality monitoring
  • Artifact detection (clicks, pops, distortion)

Use Cases: - Live performance monitoring - Recording quality check - Automatic error detection in mastering - Hardware failure prediction

TAREA 08: Personalization 🔴 PLANNING

Adaptive processing based on user behavior

  • User interaction tracking
  • Preference modeling (collaborative filtering)
  • Context-aware processing (genre, time of day, mood)
  • Reinforcement learning for adaptive effects

Use Cases: - Auto-adjust EQ based on listening history - Smart compression that learns user style - Genre-adaptive processing chains - Mood-based effect recommendations

TAREA 09: Audio Restoration 🔴 PLANNING

ML-based restoration of degraded audio

  • Declipping and distortion removal
  • Bandwidth extension (audio super-resolution)
  • Missing sample reconstruction
  • Historical recording enhancement

Use Cases: - Vinyl/tape restoration - Low-bitrate audio upsampling - Clipped audio recovery - Historical archive restoration

🔧 Technical Stack

ML Frameworks

  • ONNX Runtime: Cross-platform inference
  • TensorFlow Lite: Mobile/embedded deployment
  • LibTorch: PyTorch C++ API for advanced models
  • OpenVINO: Intel CPU/GPU optimization

Model Formats

  • ONNX (Open Neural Network Exchange)
  • TFLite (TensorFlow Lite)
  • CoreML (Apple platforms)
  • Custom binary formats for optimized inference

Audio ML Libraries

  • librosa (Python): Feature extraction
  • essentia: Audio analysis and MIR
  • torch-audiomentations: Data augmentation
  • nnAudio: Differentiable audio processing

📊 Integration Points

With Other Subsystems

05_04_DSP_PROCESSING - ML models as DSP effect plugins - Differentiable DSP for training

05_11_GRAPH_SYSTEM - ML nodes in audio graphs - Inference as graph nodes

05_14_PRESET_SYSTEM - AI-generated presets - Preset recommendation engine

05_16_PERFORMANCE_VARIANTS - Optimized inference (SIMD, GPU) - Real-time ML processing

05_25_AI_ORCHESTRATOR - Model orchestration and deployment - Training pipeline integration

🎯 Performance Targets

Real-Time Constraints

  • Latency: < 10ms for real-time effects
  • CPU Usage: < 15% per ML node (on modern CPU)
  • Memory: < 100MB per loaded model

Optimization Techniques

  • Quantization: INT8, FP16 inference
  • Pruning: Remove redundant weights
  • Knowledge Distillation: Smaller student models
  • SIMD/GPU Acceleration: Optimize tensor operations

📚 Research References

Key Papers

  1. WaveNet (van den Oord et al., 2016) - Neural audio synthesis
  2. NSynth (Engel et al., 2017) - Neural audio synthesis with WaveNet
  3. DDSP (Engel et al., 2020) - Differentiable digital signal processing
  4. Spleeter (Deezer, 2019) - Source separation
  5. Demucs (Défossez et al., 2021) - Hybrid spectrogram/waveform separation
  6. PANN (Kong et al., 2020) - Audio pattern recognition

Datasets

  • NSynth: 300k musical notes (Google Magenta)
  • MUSDB18: Multi-track music separation
  • FSD50K: Sound event classification
  • Speech Commands: Keyword spotting

🚀 Development Roadmap

Phase 1: Foundation (Q1 2025)

  • TAREA 00: ML Framework core
  • ONNX Runtime integration
  • Model loader and serializer
  • Basic inference engine

Phase 2: Core Features (Q2 2025)

  • TAREA 05: Noise Reduction
  • TAREA 03: Classification
  • TAREA 07: Anomaly Detection

Phase 3: Advanced Features (Q3 2025)

  • TAREA 04: Source Separation
  • TAREA 01: Audio Generation
  • TAREA 02: Parameter Optimization

Phase 4: Intelligence (Q4 2025)

  • TAREA 06: Preset Generation
  • TAREA 08: Personalization
  • TAREA 09: Audio Restoration

📖 Documentation

Each TAREA contains: - README.md: Detailed implementation plan - include/: C++ headers - src/: Implementation files - tests/: Unit and integration tests - examples/: Usage examples - models/: Pre-trained model files - data/: Sample datasets for testing

🔗 Dependencies

External Libraries

find_package(onnxruntime REQUIRED)
find_package(TensorFlowLite REQUIRED)
find_package(Torch REQUIRED)  # LibTorch
find_package(OpenVINO REQUIRED)

Internal Dependencies

  • 05_04_DSP_PROCESSING - Audio processing primitives
  • 05_11_GRAPH_SYSTEM - Graph integration
  • 05_16_PERFORMANCE_VARIANTS - SIMD optimization
  • 05_25_AI_ORCHESTRATOR - Model orchestration

📝 License

AudioLab 2024 - Machine Learning Subsystem

👥 Contributors

  • ML Architecture Design: AudioLab Team
  • Implementation: TBD

Last Updated: 2025-10-15 Status: 🔴 Planning Phase - Ready for implementation