Abstract: A novel scheme of wavelet thresholding for speech signal de-noising was proposed. The scheme introduces a new thresholding function to overcome the drawbacks of traditional soft and hard thresholding functions. This newly introduced function is continuous at the threshold, so that it can curb the pseudogibbs phenomena effectively. Besides, it can reduce constant bias between the original wavelet coefficient and the estimated one, which helps to preserve the feature of the signal. With this threshold function, an adaptively selective algorithm of wavelet threshold was then proposed. This algorithm has both the virtues of spatial selective noise filtration method and the traditional threshold denoising method. It takes advantage of the inter-scale correlations of the wavelet coefficients to estimate the energy of scaled noise signal. In this algorithm, the threshold in each scale is selected based on the scaled noise energy. The simulations demonstrate that the proposed method is superior to some other existing adaptive wavelet de-noising methods.  

Key words: speech signal, de-noising, wavelet transform, noise energy, adaptive thresholding