Audio Restoration and Enhancement

Audio restoration estimates a cleaner or more task-useful signal from degraded recordings. Noise, reverberation, clipping and packet loss represent different degradation models.

Noise reduction

Spectral subtraction and Wiener filtering can suppress stationary or slowly varying noise when noise statistics are estimated reliably. Poor estimates may introduce musical-noise artefacts.

Learned denoisers can handle more complex noise but may also suppress signal components outside the training distribution.

Echo and microphone arrays

Acoustic echo cancellation adaptively models the path from loudspeaker to microphone. Multi-microphone beamforming uses phase and delay differences to reinforce sound arriving from selected directions.

Packet-loss concealment estimates missing samples, while declipping attempts to reconstruct saturated regions.

Task-oriented evaluation

Perceptual improvement does not always improve downstream models. In ASR, speaker recognition or acoustic-event detection, enhancement should be evaluated together with task metrics.

For forensic use, the original recording remains separate, while filter parameters, software version and derived copies are preserved as part of the processing chain.