Wavelets and audio data compression

Abstract- In this work we present an audio data compression software package which uses a Discrete Wavelet Transform based compression procedure. The used wavelet function can be chosen from the well known Daubechies class of compactly supported wavelets. Our implementation uses an adaptive manner for the wavelet domain threshold value computation. The package has two major components: a compression software which reads a standard audio data file and the reconstruction software which reads a compressed file and generates a standard audio file. Experimental results are also presented. I. DATA COMPRESSION WITH WAVELETS Data compression is a very largely used procedure for data storage purposes. There exist a large variety of compression algorithms, many of them standardised and each of them having its advantages and its backdraws. They offers speed, high compression ratio, portability etc., but there is not a single algorithm which best fit to all kind of applications. They were classified in two major categories, the first includes the ones for compression without loss and the second one includes those for compression with loss. All the algorithms have the same purpose, to reduce the information redundancy from the considered data set, but those from the first class allow a perfect reconstruction while those from the second class does not. Those procedures which allow small information losses can achieve higher compression ratio, but they can not be utilised in applications where the data set integrity is primordial (for file compression for example). This second class includes various voice and image compression algorithms. In this work we present a complete audio compression/decompression software package, which implements an algorithm from this second class, which exploits a very popular orthogonal transform named the Discrete Wavelet Transform (DWT), [1], [2]. The topics of the wavelet transforms and their properties had been studied very extensively by the authors in the context of various data compression and signal to noise (SNR) enhancemen