TAREA 02: Parameter Optimization - Automatic DSP Tuning¶

Status: 🔴 PLANNING - Architecture defined, ready for implementation

🎯 Purpose¶

Automatic optimization of DSP parameters using machine learning, including gradient-free optimization (genetic algorithms, particle swarm), differentiable DSP for gradient-based tuning, and multi-objective optimization.

🏗️ Architecture¶

05_26_02_parameter_optimization/
├── include/
│   ├── ParameterOptimizer.h       # Main optimizer interface
│   ├── GeneticOptimizer.h         # Genetic algorithm
│   ├── ParticleSwarmOptimizer.h   # PSO
│   ├── BayesianOptimizer.h        # Bayesian optimization
│   ├── DifferentiableDSP.h        # Gradient-based optimization
│   ├── ObjectiveFunction.h        # Fitness/loss functions
│   └── PresetInterpolator.h       # Preset morphing
├── src/
│   ├── GeneticOptimizer.cpp
│   ├── ParticleSwarmOptimizer.cpp
│   ├── BayesianOptimizer.cpp
│   ├── DifferentiableDSP.cpp
│   └── MultiObjectiveOptimizer.cpp
├── tests/
│   ├── test_genetic.cpp
│   ├── test_differentiable_dsp.cpp
│   └── benchmark_optimization.cpp
└── examples/
    ├── auto_compressor.cpp        # Auto-tune compressor
    ├── eq_matching.cpp            # EQ curve matching
    └── reverb_optimization.cpp

🔑 Key Features¶

1. Gradient-Free Optimization¶

Genetic Algorithms: Population-based evolutionary search
Particle Swarm Optimization: Swarm intelligence
Bayesian Optimization: Gaussian process-based search
Simulated Annealing: Probabilistic optimization

2. Gradient-Based Optimization¶

Differentiable DSP: Backprop through audio processing
Adam/SGD optimizers: Fast convergence
Automatic differentiation: Compute gradients automatically

3. Multi-Objective Optimization¶

Pareto frontier: Trade-off between objectives
Weighted sum: Balance sound quality + CPU usage
NSGA-II: Non-dominated sorting genetic algorithm

4. Preset Generation¶

Interpolation: Smooth preset morphing
Style transfer: Apply characteristics of reference audio
Preset recommendation: ML-based suggestions

📋 Implementation Plan¶

Phase 1: Genetic Algorithm Optimizer (Week 1-2)¶

class GeneticOptimizer : public IParameterOptimizer {
public:
    struct Config {
        int population_size = 100;
        int num_generations = 50;
        float mutation_rate = 0.1f;
        float crossover_rate = 0.7f;
        SelectionMethod selection = SelectionMethod::Tournament;
    };

    // Optimize parameters to match target
    ParameterSet optimize(
        const IAudioProcessor& processor,
        const ObjectiveFunction& objective,
        const ParameterBounds& bounds
    );

private:
    std::vector<Individual> population_;
    std::mt19937 rng_;

    void initializePopulation(const ParameterBounds& bounds);
    void selection();
    void crossover();
    void mutation();
};

Phase 2: Particle Swarm Optimization (Week 3)¶

class ParticleSwarmOptimizer : public IParameterOptimizer {
public:
    struct Config {
        int num_particles = 50;
        int num_iterations = 100;
        float inertia = 0.7f;
        float cognitive = 1.5f;
        float social = 1.5f;
    };

    ParameterSet optimize(
        const IAudioProcessor& processor,
        const ObjectiveFunction& objective,
        const ParameterBounds& bounds
    );

private:
    struct Particle {
        ParameterSet position;
        ParameterSet velocity;
        ParameterSet best_position;
        float best_fitness;
    };

    std::vector<Particle> swarm_;
    ParameterSet global_best_;
};

Phase 3: Differentiable DSP (Week 4-5)¶

class DifferentiableDSP : public IParameterOptimizer {
public:
    // Optimize using gradient descent
    ParameterSet optimize(
        const IAudioProcessor& processor,
        const std::vector<float>& target_audio,
        const std::vector<float>& input_audio,
        int num_iterations = 1000
    );

    // Compute gradients via automatic differentiation
    ParameterGradients computeGradients(
        const IAudioProcessor& processor,
        const ParameterSet& params,
        const std::vector<float>& loss_gradient
    );

private:
    std::unique_ptr<Optimizer> optimizer_;  // Adam, SGD, etc.
};

Phase 4: Multi-Objective Optimization (Week 6)¶

class MultiObjectiveOptimizer {
public:
    struct Objectives {
        float sound_quality;  // Perceptual loss
        float cpu_usage;      // Real-time performance
        float latency;        // Processing delay
    };

    // Find Pareto-optimal solutions
    std::vector<ParameterSet> optimizePareto(
        const IAudioProcessor& processor,
        const MultiObjectiveFunction& objectives,
        const ParameterBounds& bounds
    );

    // Weighted sum approach
    ParameterSet optimizeWeighted(
        const IAudioProcessor& processor,
        const MultiObjectiveFunction& objectives,
        const ParameterBounds& bounds,
        const std::vector<float>& weights
    );
};

📖 Usage Examples¶

Example 1: Auto-Tune Compressor¶

#include "GeneticOptimizer.h"
#include "CompressorDSP.h"

int main() {
    CompressorDSP compressor;
    GeneticOptimizer optimizer;

    // Define target characteristics
    ObjectiveFunction objective = [](const std::vector<float>& output) {
        float target_rms = 0.5f;
        float target_crest_factor = 6.0f;

        float actual_rms = computeRMS(output);
        float actual_crest = computeCrestFactor(output);

        return std::abs(actual_rms - target_rms) +
               std::abs(actual_crest - target_crest);
    };

    // Define parameter bounds
    ParameterBounds bounds;
    bounds.add("threshold", -60.0f, 0.0f);
    bounds.add("ratio", 1.0f, 20.0f);
    bounds.add("attack", 0.1f, 100.0f);    // ms
    bounds.add("release", 10.0f, 1000.0f); // ms

    // Optimize
    auto optimal_params = optimizer.optimize(
        compressor,
        objective,
        bounds
    );

    std::cout << "Optimal threshold: " << optimal_params["threshold"] << " dB\n";
    std::cout << "Optimal ratio: " << optimal_params["ratio"] << ":1\n";
}

Example 2: EQ Matching¶

#include "DifferentiableDSP.h"
#include "ParametricEQ.h"

int main() {
    ParametricEQ eq;
    DifferentiableDSP optimizer;

    // Load target audio (reference track)
    auto target_audio = loadAudio("reference.wav");

    // Load input audio (to be EQ'd)
    auto input_audio = loadAudio("input.wav");

    // Optimize EQ to match target spectrum
    auto optimal_params = optimizer.optimize(
        eq,
        target_audio,
        input_audio,
        1000  // iterations
    );

    // Apply optimized EQ
    eq.setParameters(optimal_params);
    auto output = eq.process(input_audio);
    saveAudio("output_matched.wav", output);
}

📚 Research References¶

Differentiable Digital Signal Processing (Engel et al., 2020)
NSGA-II (Deb et al., 2002) - Multi-objective optimization
Bayesian Optimization (Snoek et al., 2012)
AutoML for Audio - Automated hyperparameter tuning

🚀 Roadmap¶

Week 1-2: Genetic Algorithms¶

Population management
Fitness evaluation
Selection, crossover, mutation

Week 3: PSO¶

Particle swarm implementation
Convergence criteria

Week 4-5: Differentiable DSP¶

Gradient computation
Adam optimizer integration

Week 6: Multi-Objective¶

Pareto frontier computation
Trade-off visualization

Last Updated: 2025-10-15 Status: 🔴 Ready for implementation Priority: 🔥 High - Critical for intelligent automation