The optimized wavelet filters for speech compression

In this paper, optimized wavelet filters for speech compression are proposed whose wavelet filter coefficients are derived with different window techniques such as Kaiser and Blackman windows via simple linear optimization. When the developed wavelet filters are exploited for speech compression, they not only give better compression ratio but also yield good fidelity parameters as compared to other wavelet filters. A comparative study of performance of different existing wavelet filters and the proposed wavelet filters is made in terms of compression ratio (CR), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and normalized root-mean square error (NRMSE) at different thresholding levels. The simulation result included in this paper shows increased efficacy and improved performance of the proposed filters in the field of speech signal processing.

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