[feature] adaptive noise reduction

[louis030195]

Here are 10 approaches to implement adaptive noise reduction, ordered by complexity/effectiveness:

1. Enhanced Spectral Subtraction

• Track noise floor during silence periods
• Use overlapping windows for smoother transitions
• Apply different thresholds for different frequency bands
• Implement oversubtraction factor based on SNR

  audio-processing #signal-processing

2. Wiener Filtering

• Estimate noise and speech power spectra
• Update noise estimate during non-speech segments
• Use MMSE estimation for optimal filtering
• Apply psychoacoustic masking

  dsp #filtering

3. Kalman Filtering Approach

• Model speech as AR process
• Track and update noise statistics in real-time
• Use multiple states for different noise types
• Implement adaptive step size

  state-estimation #adaptive-filtering

4. Neural Network Based

• Train on paired noisy/clean audio
• Use LSTM for temporal dependencies
• Real-time inference for streaming
• Continuous adaptation to new noise types

  machine-learning #deep-learning

5. Wavelet Domain Processing

• Decompose signal into subbands
• Apply different strategies per band
• Use soft thresholding
• Adaptive threshold selection

  wavelets #multiresolution

6. Subspace Methods

• Eigenvalue decomposition of correlation matrix
• Separate signal and noise subspaces
• Track subspace changes over time
• Apply projection methods

  linear-algebra #subspace-tracking

7. Multi-Channel Processing

• Use multiple input streams
• Beamforming for spatial filtering
• Cross-correlation for noise estimation
• Adaptive array processing

  array-processing #beamforming

8. NMF-based Approach

• Learn noise dictionary during silence
• Update dictionary adaptively
• Sparse reconstruction
• Online dictionary learning

  matrix-factorization #sparse-coding

9. Psychoacoustic Model

• Incorporate human hearing characteristics
• Masking threshold computation
• Critical band processing
• Temporal masking effects

  psychoacoustics #perceptual-audio

10. Hybrid Approach

• Combine multiple methods
• Context-dependent method selection
• Adaptive weighting of different algorithms
• Quality-based method switching

  hybrid-systems #adaptive-processing

Key considerations for your 24/7 recording case:

• Low latency requirements
• Computational efficiency
• Memory usage
• Continuous adaptation
• Robustness to different environments

  system-design #performance

Would you like me to elaborate on any of these approaches or discuss specific implementation details for your use case?

[linear[bot]]

MED-240 [feature] adaptive noise reduction