TAREA 01: Audio Generation - Neural Audio Synthesis

Status: 🔴 PLANNING - Architecture defined, ready for implementation

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🎯 Purpose

Neural audio synthesis and generation using deep learning models including WaveNet, SampleRNN, NSynth, DDSP, and generative models (GANs, VAEs, Diffusion Models).

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🏗️ Architecture

05_26_01_audio_generation/
├── include/
│   ├── AudioGenerator.h           # Main generator interface
│   ├── WaveNetGenerator.h         # WaveNet synthesis
│   ├── SampleRNNGenerator.h       # SampleRNN synthesis
│   ├── NSynthGenerator.h          # NSynth (Neural Synthesis)
│   ├── DDSPGenerator.h            # Differentiable DSP
│   ├── GANGenerator.h             # GAN-based generation
│   ├── VAEGenerator.h             # VAE-based generation
│   └── DiffusionGenerator.h       # Diffusion model generation
├── src/
│   ├── WaveNetGenerator.cpp
│   ├── DDSPGenerator.cpp
│   ├── ConditionalGeneration.cpp  # Conditional synthesis
│   └── TimbreTransfer.cpp         # Style/timbre transfer
├── tests/
│   ├── test_wavenet.cpp
│   ├── test_ddsp.cpp
│   └── benchmark_generation.cpp
├── examples/
│   ├── simple_synthesis.cpp
│   ├── timbre_transfer.cpp
│   └── conditional_generation.cpp
└── models/
    ├── wavenet_speech.onnx
    ├── ddsp_violin.onnx
    └── nsynth_pretrained.onnx

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🔑 Key Features

1. Neural Oscillators

• WaveNet: Autoregressive waveform generation
• SampleRNN: Hierarchical RNN synthesis
• WaveGlow: Flow-based generative model

2. Timbre Transfer

• NSynth: Neural audio synthesis with timbre interpolation
• DDSP: Differentiable DSP for interpretable synthesis
• Style Transfer: Apply style of one audio to another

3. Generative Models

• GANs: GAN-based synthesis (WaveGAN, GANSynth)
• VAEs: Variational autoencoders for latent space exploration
• Diffusion Models: Denoising diffusion probabilistic models

4. Conditional Synthesis

• Text-to-audio
• MIDI-to-audio
• Pitch/loudness conditioning
• Multi-modal conditioning

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📋 Implementation Plan

Phase 1: WaveNet Generator (Week 1-2)

1.1 WaveNet Architecture

class WaveNetGenerator : public IAudioGenerator {
public:
    struct Config {
        int num_layers = 30;
        int num_blocks = 3;
        int num_channels = 256;
        int kernel_size = 2;
        int sample_rate = 16000;
    };

    bool initialize(const Config& config);

    // Generate audio from conditioning
    std::vector<float> generate(
        const Conditioning& condition,
        int num_samples
    );

    // Fast generation (parallel WaveNet)
    std::vector<float> generateFast(
        const Conditioning& condition,
        int num_samples
    );

private:
    std::unique_ptr<IInferenceEngine> engine_;
    Config config_;
};

1.2 Conditioning Interface

struct Conditioning {
    // Acoustic features
    std::optional<std::vector<float>> mel_spectrogram;
    std::optional<std::vector<float>> f0;  // Pitch
    std::optional<std::vector<float>> loudness;

    // Categorical conditioning
    std::optional<int> speaker_id;
    std::optional<int> instrument_class;

    // Text (for TTS)
    std::optional<std::string> text;
    std::optional<std::vector<int>> phonemes;
};

Phase 2: DDSP (Differentiable DSP) (Week 3-4)

2.1 DDSP Architecture

class DDSPGenerator : public IAudioGenerator {
public:
    struct Harmonics {
        std::vector<float> amplitudes;  // Harmonic amplitudes
        std::vector<float> frequencies; // Harmonic frequencies
    };

    struct NoiseParams {
        std::vector<float> filtered_noise;
    };

    // Generate from interpretable parameters
    std::vector<float> synthesize(
        const Harmonics& harmonics,
        const NoiseParams& noise,
        const std::vector<float>& f0,
        const std::vector<float>& loudness
    );

    // Timbre transfer
    std::vector<float> transferTimbre(
        const std::vector<float>& input_audio,
        const std::string& target_timbre_model
    );

private:
    std::unique_ptr<HarmonicSynthesizer> harmonic_synth_;
    std::unique_ptr<NoiseGenerator> noise_gen_;
    std::unique_ptr<ReverbModule> reverb_;
};

2.2 DDSP Components

// Harmonic synthesizer (additive synthesis)
class HarmonicSynthesizer {
public:
    std::vector<float> synthesize(
        const std::vector<float>& amplitudes,
        const std::vector<float>& frequencies,
        float sample_rate,
        int num_samples
    );
};

// Filtered noise generator
class NoiseGenerator {
public:
    std::vector<float> generate(
        const std::vector<float>& filter_coeffs,
        int num_samples
    );
};

Phase 3: NSynth Generator (Week 5-6)

3.1 NSynth Interface

class NSynthGenerator : public IAudioGenerator {
public:
    // Generate note with specific pitch and instrument
    std::vector<float> generateNote(
        float pitch,         // MIDI note number
        float velocity,
        float duration_sec,
        int instrument_id
    );

    // Interpolate between two timbres
    std::vector<float> interpolateTimbre(
        float pitch,
        float velocity,
        float duration_sec,
        int instrument_a,
        int instrument_b,
        float mix  // 0.0 = A, 1.0 = B
    );

    // Generate from latent code
    std::vector<float> generateFromLatent(
        const std::vector<float>& latent_code,
        float pitch,
        float velocity,
        float duration_sec
    );

private:
    std::unique_ptr<WaveNetDecoder> decoder_;
    std::unique_ptr<LatentEncoder> encoder_;
};

Phase 4: GAN-Based Generation (Week 7-8)

4.1 WaveGAN

class WaveGANGenerator : public IAudioGenerator {
public:
    // Generate from random noise
    std::vector<float> generateRandom(int num_samples);

    // Generate from specific latent vector
    std::vector<float> generateFromLatent(
        const std::vector<float>& latent_vector
    );

    // Interpolate in latent space
    std::vector<float> interpolate(
        const std::vector<float>& latent_a,
        const std::vector<float>& latent_b,
        float t  // Interpolation factor [0, 1]
    );

private:
    std::unique_ptr<IInferenceEngine> generator_;
    int latent_dim_ = 100;
};

4.2 GANSynth (Spectrogram GAN)

class GANSynthGenerator : public IAudioGenerator {
public:
    // Generate spectrogram then convert to audio
    std::vector<float> generate(
        float pitch,
        const std::vector<float>& latent_vector
    );

private:
    std::unique_ptr<IInferenceEngine> generator_;
    std::unique_ptr<PhaseReconstructor> phase_reconstructor_;
};

Phase 5: Diffusion Models (Week 9-10)

5.1 Diffusion-Based Synthesis

class DiffusionGenerator : public IAudioGenerator {
public:
    struct DiffusionConfig {
        int num_diffusion_steps = 50;
        float noise_schedule_start = 1e-4f;
        float noise_schedule_end = 0.02f;
    };

    // Generate audio via iterative denoising
    std::vector<float> generate(
        const Conditioning& condition,
        int num_samples,
        int num_steps = 50
    );

    // Fast sampling (DDIM, DPM-Solver)
    std::vector<float> generateFast(
        const Conditioning& condition,
        int num_samples,
        int num_steps = 10
    );

private:
    std::unique_ptr<IInferenceEngine> denoiser_;
    DiffusionConfig config_;
};

---

🧪 Testing Strategy

Quality Metrics

• Perceptual quality: PESQ, POLQA (speech)
• Spectral distance: Log-spectral distance
• Reconstruction accuracy: MSE, STFT loss
• Listening tests: MOS (Mean Opinion Score)

Performance Tests

• Real-time factor (RTF): generation_time / audio_duration
• Latency: time from request to first sample
• Memory usage during generation

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📊 Performance Targets

Generation Speed

• WaveNet: 0.1x real-time (fast WaveNet: 1x real-time)
• DDSP: 10x real-time (highly efficient)
• GAN: 100x real-time
• Diffusion: 0.5x real-time (50 steps), 2x real-time (10 steps, fast sampler)

Quality

• Perceptual quality: > 4.0 MOS (1-5 scale)
• Naturalness: Comparable to natural recordings for speech/music

---

📖 Usage Examples

Example 1: DDSP Timbre Transfer

#include "DDSPGenerator.h"

int main() {
    DDSPGenerator generator;
    generator.initialize("models/ddsp_violin.onnx");

    // Load input audio (e.g., singing voice)
    auto input_audio = loadAudio("input_voice.wav");

    // Transfer timbre to violin
    auto output = generator.transferTimbre(
        input_audio,
        "models/ddsp_violin.onnx"
    );

    saveAudio("output_violin.wav", output, 16000);
}

Example 2: NSynth Interpolation

#include "NSynthGenerator.h"

int main() {
    NSynthGenerator generator;
    generator.initialize("models/nsynth_pretrained.onnx");

    // Interpolate between piano and guitar
    int piano_id = 0;
    int guitar_id = 5;

    for (float mix = 0.0f; mix <= 1.0f; mix += 0.1f) {
        auto audio = generator.interpolateTimbre(
            60.0f,      // Middle C
            0.8f,       // Velocity
            2.0f,       // Duration
            piano_id,
            guitar_id,
            mix
        );

        std::string filename = "interpolation_" +
                              std::to_string(int(mix * 10)) + ".wav";
        saveAudio(filename, audio, 16000);
    }
}

Example 3: WaveGAN Synthesis

#include "GANGenerator.h"

int main() {
    WaveGANGenerator generator;
    generator.initialize("models/wavegan_drums.onnx");

    // Generate random drum sounds
    for (int i = 0; i < 10; ++i) {
        auto audio = generator.generateRandom(16000);  // 1 second
        saveAudio("drum_" + std::to_string(i) + ".wav", audio, 16000);
    }
}

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🔗 Dependencies

External Libraries

• ONNX Runtime: Model inference
• 05_26_00_ml_framework: ML infrastructure
• 05_04_DSP_PROCESSING: Audio processing (FFT, resampling)
• librosa (optional): Feature extraction for conditioning

Pre-trained Models

• WaveNet: Speech synthesis models
• DDSP: Various instrument models (violin, flute, trumpet)
• NSynth: Multi-instrument generative model
• WaveGAN: Drum synthesis, texture generation

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📚 Research References

1. WaveNet (van den Oord et al., 2016) - Autoregressive audio generation
2. NSynth (Engel et al., 2017) - Neural audio synthesis with WaveNet
3. DDSP (Engel et al., 2020) - Differentiable digital signal processing
4. WaveGAN (Donahue et al., 2019) - Adversarial audio synthesis
5. DiffWave (Kong et al., 2021) - Diffusion probabilistic model for waveforms

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🚀 Roadmap

Week 1-2: WaveNet

• WaveNet architecture implementation
• Conditioning interface
• Fast WaveNet (parallel generation)

Week 3-4: DDSP

• Harmonic synthesizer
• Noise generator
• Timbre transfer pipeline

Week 5-6: NSynth

• NSynth decoder integration
• Timbre interpolation
• Latent space exploration

Week 7-8: GANs

• WaveGAN generator
• GANSynth (spectrogram-based)
• Latent space interpolation

Week 9-10: Diffusion Models

• Diffusion model integration
• Fast sampling (DDIM, DPM-Solver)
• Conditional generation

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Last Updated: 2025-10-15 Status: 🔴 Ready for implementation Priority: 🔥 High - Core feature for neural synthesis