Compressive Sensing and Contourlet Transform Applications in Speech Signal

Abstract

This paper explains a new method for performing two different processes compact and encode in a single algorithm. Speech compression is the way toward changing over discourse signals into a structure that is neatly packed so it has good quality in performance for correspondence and capacity by minimizing the dimensions of the data without losing the information standard (quality) of the original speech. On the other hand Speech encryption is the process of converting usual formal into an unrecognized format to give security to the data across an insecure channel in the transmitter. These two processes can be achieved by a compressive sensing algorithm. In addition to compressive sensing, the transformation of the outline is advantage to demonstrate the compressive sensing concept. It is a two-dimensional transform method for image representations. Contourlet transform plays an important for representing the sparse signals in the signal.