Abstract
Compressive sensing is a technique for data acquisition that promises sampling a sparse signal from a far fewer measurements than its dimension. In this paper for speech signal Hello of length n=16000 is reconstructed using Orthogonal Matching Pursuit. For better reconstruction an effort is made to fix the size of sensing matrix φ. Factors such as mean square error (MSR), signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) are taken into considerations to measure the performance of the reconstructed signal.
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