To Study the Effects of Basis and Sensing Matrix on Compressive Sensing

Abstract

Compressive sensing (CS) is the appropriate way to recover the compressible signal with very few observations or precisely very little number of measurements rather than the conventional methods. As per the theory given by Shannon for proper recovery of a signal the sampling frequency must be greater than or equal to the largest frequency component in that signal. So the storage requirement to store the data according to the Nyquist theorem is too high. So compressive sensing is used to reduce this storage requirement. There are two important parameters one is sensing matrix and another is measurement matrix by changing these two parameters we can change the quality of the recovered signal. There are various reconstruction algorithms which are used for proper reconstruction of signal. The work which is done in this paper comprises of various music signals on which the compressive sensing applied. As per the result the single tone music signal have less value of MSE than the multi tone and vocal song signal. The SNR value is quiet good for single tone than the multi tone & vocal song. This is due to the single frequency component in the single tone music signal.