Wavelet based speech enhancement using two different threshold-based denoising algorithms

In this paper, we present a wavelet-based speech denoising technique obtained by the combination of the μ-law thresholding and the soft thresholding algorithm. Denoising is a compromise between the removal of the largest possible amount of noise and the preservation of signal integrity. To achieve a good implementation of this compromise we purpose the following procedure. The signal to be denoised is decomposed using wavelet packets up to the seventh level using DB11 wavelets. The μ-law thresholding is applied to all the final decomposition level subband coefficients except those of the two lower subbands on which soft thresholding is applied. To evaluate the performance of the proposed method, a clean speech dataset from the TIMIT database, corrupted with pink noise, for SNR levels ranging from 5 to 15 dB has been utilized. It has been found that the results obtained by our method are better than those given by each one of the two combined methods used separately.