Speech Enhancement using Maximal Overlap Discrete Wavelet Transform Method

Abstract

Signal denoising for non-stationary digital signals can be effectively succeeded by using discrete wavelet transform. Selecting of a suitable thresholding method is important to minimize the loss of useful signal information. This paper demonstrates the application of the maximal overlap wavelet transform (Modwt) technique in speech signal denoising. The analysis algorithm was performed on Matlab platform. In this algorithm, different kinds of input noisy speech signals having environmental background noises such as restaurant, car, street or station were tested. The noisy signals were filtered from the speech signal by thresholding of wavelet coefficients with threshold estimation methods known as sgtwolog, modwtsqtwolog, heursure, rigrsure and minimax. The performance of the Modwt in denoising process was evaluated by comparing signal-to noise ratio (SNR) and mean square error (MSE) results to those of wellknown threshold estimation methods. First, denoising effectiveness of a Modwt based threshold method was tested in different scenarios and very important improvements in denoising process were achieved by Modwt based scenarios. Next, the influence of the different wavelets families on Modwt based threshold estimation method was evaluated by experimental results. The results revealed that Modwt based method outperforms conventional threshold methods while providing nearly up to a %24 increase in SNR value.