Speech De-noising and Compression using Wavelet Transform

Speech compression is an area of digital processing that is focusing on reducing bit rate of the speech signal for transmission or storage without significant loss of quality. The focus of this paper is to compress the digital speech using wavelet transform. The uncompressed digital speech requires huge amount of memory. The main idea behind the speech compression algorithm is to represent this uncompressed speech with minimum number of bits and optimum speech quality. Wavelet transform has been recently proposed for signal analysis. The wavelet transform is useful to remove redundancies and irrelevancies present in the speech signal for the compact representation. The classical subband coding method also known as filter bank method is used in which signal is decomposed into basis of wavelet functions using high pass and low pass filters. Speech coding is a lossy scheme and is implemented here to compress one-dimensional speech signal. Basically, this scheme consists of three operations which are the transform, threshold techniques (by level and global threshold), and run-length encoding operations. Finally the compressed signal is reconstructed. The speech compression using filter bank method is implemented and simulated by using wavelet transform algorithm for different wavelets such as Haar, Daubechies series (Daub-4, Daub-6, Daub-8, Daub-12, Daub-16) and bi-orthogonal wavelet. Key-Words: Speech compression, Wavelet, Run Length Encode, Filter bank, Noise