Basis & Sensing Matrix as key effecting Parameters for Compressive Sensing

Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signals using very minute observations. These minute observations are also called the number of measurement. The basic benefits of CS are that the number of measurements which are required for proper reconstruction of the compressed signal is very less than the conventional method. If we go through the literature then, we get that for proper reconstruction of signal a theory is given by Shannon. This theory states that the sampling frequency must be higher than twice the highest frequency component in that signal. So the limitation of the conventional method is that it requires so much storage to store and a large bandwidth to transmit the data. So researchers came with a new idea which is termed as Compressive Sensing. Key effecting parameters which are very crucial for the compressive sensing is the Basis and Sensing matrix. The basic fact behind this approach is that the signal which is used for the compression and reconstruction must be Sparse. In the analysis which is done by us in this paper is that the change in these two matrices directly changes the value of SNR which will be obtained after compression and reconstruction using the compressive sensing. The work which is carried out based on three kinds of music signals with different cases of Basis and Sensing matrices.