Studies on Implementation of Harr and daubechies Wavelet for Denoising of Speech Signal
Mahesh S. Chavan, Nikos E. Mastorakis · 2010
In hands free speech communication environments situation occurs that speech is superposed by background noise. Over the past few decades there is tremendous increase in the level of ambient environmental noise. This has been due to growth of technology. Noise is added by various factors like noisy engines, heavy machines, pumps, vehicles, over noisy telephone channel or using radio communication device in an aircraft cockpit. As speech is transmitted and received using various media it introduces distortions and have bandwidth constraints. These degradations lower intelligibility of speech message causing severe problems in downstream processing and user perception of speech signal. There has been a lot of research in speech denoising so far but there always remains room for improvement. The motivation to use wavelet as a possible alternative is to explore new ways to reduce computational complexity and to achieve better noise reduction performance. The wavelet denoising technique is called thresholding. It is divided in three steps. The first one consists in computing the coefficients of the wavelet transform (WT) which is a linear operation. The second one consists in thresholding these coefficients. The last step is the inversion of the thresholded coefficients by applying the inverse wavelet transform, which leads to the denoised signal. This technique is simple and efficient. In this paper wavlet is used as denoising algorithm. Performance of the Haar and Daubechies wavelets are experimentally evaluated.