Comparative analysis of basis & measurement matrices for non-speech audio signal using compressive sensing

Compressive sensing is the concept of reducing sampling rate of a signal. It reduces the required number of samples for signal representation at much lower rate than Nyquist's rate. High speed applications require high sampling rate that overburdens the role of ADC in signal processing. So in such cases compressive sensing plays a major role for improvement of performance. The work is based on music signal (non-speech audio signal) by iterating on various combinations of the sensing matrix and basis matrix to find the best suited for desired application. One of the sensing matrix provides the best incoherence and Restricted Isometric Property (RIP) for a particular basis matrix. This combination gives an optimum value of Signal to Noise Ratio (SNR). In order to have faithful recovery, it is necessary to fulfill certain properties that require a perfect combination of basis matrix and sensing matrix. This has also analyzed in the paper.