Özet:
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.
