What is Audio Denoising

How It Works

Audio denoising models learn to separate clean speech or target audio from noise. Traditional methods use spectral subtraction or Wiener filtering. Modern neural approaches process the noisy spectrogram through a neural network that predicts either a clean spectrogram or a multiplicative mask that filters out noise. The model learns noise patterns from paired clean/noisy training examples.

Technical Details

State-of-the-art models include DNS (Deep Noise Suppression from Microsoft), Meta Demucs, and RNNoise. Architectures range from lightweight RNN models running in real-time on CPU to large U-Net models for maximum quality. Processing can operate in time domain (waveform) or frequency domain (spectrogram). Metrics include PESQ (perceptual quality), SI-SDR (signal-to-distortion ratio), and STOI (intelligibility).

Best Practices

• Apply denoising as a preprocessing step before ASR or speaker analysis for improved accuracy
• Choose model complexity based on real-time requirements versus quality needs
• Validate that denoising does not distort the target audio or introduce artifacts
• Use noise-specific models when the noise type is known (e.g., wind, traffic, HVAC)

Common Pitfalls

• Over-denoising that removes desired audio content along with noise
• Training on synthetic noise mixtures that do not represent real-world noise conditions
• Not handling non-stationary noise that changes character throughout the recording
• Applying denoising that introduces musical noise artifacts in quiet segments

Advanced Tips

• Use source separation models (Demucs) for complex mixtures with multiple sound sources
• Implement noise-aware training for downstream models as an alternative to explicit denoising
• Apply denoising to video soundtracks before extracting audio features for multimodal analysis
• Combine multiple denoising models in an ensemble for robust noise removal across conditions