Abstract
Speech enhancement using Compressive Sensing algorithm is a different paradigm from the conventional enhancement and compression techniques. Compressive Sensing requires very less number of samples compared to the Nyquist sampling for the purpose of reconstructing the signal. This work provides a comparative analysis of three sparsifying basis functions DFT, DCT and DWT for compressive speech enhancement on the basis of five objective measures. The objective measures used are: Signal to Noise Ratio, Segmental Signal to Noise Ratio, Log-Likelihood Ratio, Perceptual Evaluation of Speech Quality and processing time. Experimental analysis concludes that DWT as a basis function performs better than the others for compressive speech enhancement. As orthogonal wavelets are suitable for signal denoising, this work further investigates the performance of the four orthogonal wavelet families: Daubechies, Coiflets, Symlets and Fejer-Korovkin as basis functions for the purpose of compressive speech enhancement.
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