Skip to content

TAREA 09: Audio Restoration - ML-Based Repair

Status: 🔴 PLANNING

🎯 Purpose

Machine learning-based restoration of degraded audio including declipping, bandwidth extension, missing sample reconstruction, and historical recording enhancement.

🏗️ Key Components

  • Declipping: Reconstruct clipped audio peaks
  • Bandwidth Extension: Audio super-resolution (upsample 8kHz → 48kHz)
  • Missing Sample Reconstruction: Inpainting for dropouts
  • Historical Enhancement: Vinyl/tape restoration, noise + distortion removal

📋 Architecture

class AudioRestorer {
public:
    // Declip distorted audio
    std::vector<float> declip(const std::vector<float>& clipped_audio);

    // Extend bandwidth (super-resolution)
    std::vector<float> extendBandwidth(
        const std::vector<float>& lowband_audio,
        int target_sample_rate
    );

    // Reconstruct missing samples
    std::vector<float> reconstructMissing(
        const std::vector<float>& audio,
        const std::vector<bool>& missing_mask
    );

    // Restore historical recording
    std::vector<float> restoreHistorical(
        const std::vector<float>& degraded_audio,
        RestorationType type
    );
};

enum class RestorationType {
    Vinyl,          // Remove crackle, pops, rumble
    Tape,           // Remove hiss, wow, flutter
    LowBitrate,     // Upsample from low-quality source
    Damaged         // General restoration
};

🎯 Use Cases

  • Vinyl/tape restoration
  • Low-bitrate audio upsampling (MP3 64kbps → lossless)
  • Clipped audio recovery (from overdriven recordings)
  • Historical archive restoration
  • Damaged recording repair

📚 Techniques

  • U-Net Architecture: Encoder-decoder for restoration
  • GAN-Based Super-Resolution: Generate high-frequency content
  • Autoregressive Models: WaveNet for sample reconstruction
  • Attention Mechanisms: Focus on degraded regions

🔧 Example

AudioRestorer restorer;
restorer.initialize("models/audio_super_resolution.onnx");

// Example 1: Bandwidth extension
auto lowband_audio = loadAudio("phone_call_8khz.wav");  // 8 kHz
auto highband_audio = restorer.extendBandwidth(
    lowband_audio,
    48000  // Target: 48 kHz
);
saveAudio("restored_48khz.wav", highband_audio, 48000);

// Example 2: Declipping
auto clipped_audio = loadAudio("overdriven_guitar.wav");
auto declipped = restorer.declip(clipped_audio);
saveAudio("declipped_guitar.wav", declipped, 44100);

// Example 3: Historical restoration
auto vinyl_recording = loadAudio("old_jazz_vinyl.wav");
auto restored = restorer.restoreHistorical(
    vinyl_recording,
    RestorationType::Vinyl
);
saveAudio("restored_jazz.wav", restored, 44100);

📊 Performance Targets

  • Declipping: THD+N < 1% after restoration
  • Bandwidth Extension: Perceptual quality comparable to original
  • Speed: 5x real-time (offline processing)
  • Quality: PESQ > 3.5, POLQA > 3.8

📚 Models

  • Audio Super-Resolution: GAN-based upsampling
  • Declipping Networks: U-Net + adversarial loss
  • Vinyl Restoration: Multi-stage noise removal
  • WaveUNet: Waveform-to-waveform restoration

🔬 Research References

  1. Audio Super-Resolution (Kuleshov et al., 2017)
  2. Audio Declipping (Lagrange et al., 2019)
  3. Audio Inpainting (Marafioti et al., 2019)
  4. Historical Recording Enhancement (Godsill et al., 2002)

Priority: 🟡 Medium - Specialized but high-value feature