Enhancement of Speech Signals Using Weighted Mask and Neuro-Fuzzy Classifier

Abstract

In this paper, we present an effective noise suppression technique for enhancement of speech signals using weighted mask and Neuro-fuzzy. Initially, the noisy speech signal is broken down into various time-frequency (TF) units and the features are extracted by finding out the Modified Amplitude Magnitude Spectrogram (MAMS). The signal is then classified into respective classes based on the ratio value between the estimated spectrum value and the original spectrum value using Neuro-Fuzzy Classifier. Subsequently, in the enhancement stage, filtered waveforms are windowed and then weighted by the mask value and summed up to get the enhanced signal. We have used evaluation metrics parameters of PESQ (Perceptual Evaluation of Speech Quality), IS (Itakura–Saito distance) and MARS based composite measures in-order to evaluate the proposed technique. We have taken sound samples under various conditions from two databases. We have compared our proposed technique having different Neuro-fuzzy classifiers with the previous technique (having Bayesian classifier). We have found out that the proposed technique achieves good results. Average values obtained for proposed technique considering all noise categories at -5dB had PESQ score of 0.9738, IS score of 17.558, Mars based composite measure of 3.728. The highest values obtained by the technique for PESQ was 1.76, for IS was 96.83 and for MARS based composite measures was 4.147.