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TAREA 05: Noise Reduction - ML-Based Denoising

Status: 🔴 PLANNING

🎯 Purpose

Intelligent noise reduction using machine learning for speech enhancement, background noise removal, and artifact suppression.

🏗️ Key Components

  • Spectral Denoising: Wiener filtering + ML
  • Waveform Denoising: WaveUNet, SEGAN
  • Speech Enhancement: RNNoise, DeepFilterNet
  • Adaptive Noise Profiling: Automatic noise estimation

📋 Architecture

class MLNoiseReducer {
public:
    // Single-pass denoising
    std::vector<float> denoise(const std::vector<float>& noisy_audio);

    // Real-time denoising with state
    void denoiseRealTime(const float* input, float* output,
                        int num_samples);

    // Adaptive noise profiling
    void learnNoiseProfile(const std::vector<float>& noise_sample);

    // Control noise reduction strength
    void setReductionStrength(float strength);  // 0.0 - 1.0
};

🎯 Use Cases

  • Podcast cleanup
  • Field recording restoration
  • Live broadcast denoising
  • Voice-over cleaning
  • Video call enhancement

📊 Performance Targets

  • Quality: PESQ > 3.5, POLQA > 3.8
  • Speed: 10x real-time (offline), 1x real-time (online)
  • Latency: < 20ms for real-time mode

📚 Models

  • RNNoise: Recurrent neural network denoiser
  • DeepFilterNet: Deep filtering network
  • SEGAN: Speech enhancement GAN
  • WaveUNet: U-Net for waveforms

🔧 Implementation

// Example: Real-time speech enhancement
MLNoiseReducer reducer;
reducer.initialize("models/rnnoise.onnx");
reducer.setReductionStrength(0.8f);

// Process audio buffer
float input_buffer[512];
float output_buffer[512];
reducer.denoiseRealTime(input_buffer, output_buffer, 512);

Priority: 🔥 Critical - Essential for production-quality audio