Impulsive noise suppression using neural networks
Ilyas Potamitis, Nikos Fakotakis, G. Kokkinakis · 2002
This article presents a novel technique for suppressing the effect of impulsive noise in the context of automatic speech recognition (ASR). The noise suppression scheme is based on the cooperation of two neural networks. The first one is responsible for the detection of corrupted cepstral vectors due to the presence of impulsive noise. The second one is dedicated to the restoration of the detected problematic vectors solely. The novelty of the method lies in the robust detection of impulsive noise regardless of the noise source and the local restoration of the feature vectors. By avoiding a global act on the original waveform as spectral subtraction and Wiener filters do, we don't inflict any distortions on an already clean part of the original waveform. Extensive experimental valuation on a spoken digit database in the presence of machine gun impulsive noise has proved the robustness of our method.