Speech Restoration & Enhancement
A comparison of two classic noise-reduction methods, spectral subtraction and Wiener filtering, that starts from a failure and works out why one breaks where the other holds.
How it works
- An STFT front end feeds a noise profiler that fingerprints the first few frames. A decision step picks fan noise (steady) or rain noise (broadband) and applies the matching filter before converting back with an inverse STFT.
- Spectral subtraction estimates a noise profile and subtracts it. It is simple and works well on steady fan or AC hum.
- On broadband rain it fails, because over-subtraction floors values to zero and leaves robotic 'musical noise'. The fix is a spectral-flooring term that keeps a little comfort noise.
- Wiener filtering instead weights each frequency by how likely speech is present, so it handles rain more gracefully. A 300 to 3400 Hz telephony bandpass and noise gating finish the voice. A U-Net that masks the spectrogram was also tried.
Results
- On steady fan noise with spectral subtraction, PSNR went from 30.9 to 35.5 dB and MSE dropped to 1.8e-5.
- On rain noise the Wiener filter reached about 30.6 dB PSNR, and the flooring fix removed the robotic artefacts.
- The takeaway is that the method has to match the noise. Subtraction assumes steady noise, and rain overlaps the consonants in speech.
Slides
The full project deck. You can read it right here.