This paper presents an optimized speech compression algorithm using discrete wavelet transform, and its real time implementation on fixed-point digital signal processor (DSP). The optimized speech compression algorithm presents the advantages to ensure low complexity, low bit rate and achieve high speech coding efficiency, and this by adding a voice activity detector (VAD) module before the application of the discrete wavelet transform. The VAD module avoids the computation of the discrete wavelet coefficients during the inactive voice signal. In addition, a real-time implementation of the optimized speech compression algorithm is performed using fixed-point processor. The optimized and the original algorithms are evaluated and compared in terms of CPU time (sec), Cycle count (MCPS), Memory consumption (Ko), Compression Ratio (CR), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) and Normalized Root Mean Square Error (NRMSE).

This paper presents a new lossy compression algorithm for stationary signal based on Discrete Walsh Hadamard Transform (DWHT). The principle of compression algorithm consists in framing the original speech signal into stationary frames and applying the DWHT. Then, the obtained coefficients are thresholded in order to truncate all coefficients below a given thresholds values. Compression is achieved by efficient encoding of the string values of zeros. A comparative study of performance between the algorithms based on DWHT and Discrete Wavelet Transform (DWT) is performed in terms of some objective criteria: compression ratio (CR), signal to noise ratio, peak signal to noise ratio (SNR), normalized root mean square error (NRMSE) and CPU time. The simulation results show that the algorithm based on DWHT is characterized by a very low complexity implementation and improved CR, SNR, PSNR and NRMSE compared to the DWT algorithm and this for stationary frame.

In this paper, real-coded genetic algorithm (GA) is used for designing two-channel quadrature mirror filter (QMF) banks based on the Kaiser Window. The shape of the Kaiser window and the cutoff frequency of the prototype filter are optimized using a simple GA. The optimized QMF banks are exploited as mother wavelets for speech compression based on discret wavelet transform (DWT). The simulation results show the efficiency of the GA for designing QMF banks using adjustable windows length and especially for optimizing wavelet filters used in speech compression based on wavelets. In addition, a comparative of performance of the developed wavelets filters using GA and others known wavelets is made in term of objective criteria (CR, SNR, PSNR, and NRMSE). The simulation results show that the optimized wavelets filters outperform others wavelets already exist used for speech compression.