Implementation of Compressive Sensing for Speech Signals

摘要

The advancements in today's multimedia applications demand high quality speech signal transmission as well as storage. The limited availability of bandwidth and storage capacity necessitates the development of better compression techniques for speech signals. Compressive sensing is an emerging technique in signal processing and it provides a novel framework for speech signal compression. In compressive sensing, a signal can be exactly reconstructed if it is naturally sparse or in some sparsifying basis. In this paper, the sparsification of speech signal is done by applying Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Linear Predictive Coding (LPC). The sparsified speech signal is compressive sensed to reduce the number of samples. The original signal is reconstructed using different algorithms like Basis Pursuit (BP), l1 regularized least squares (l1 ls) and Orthogonal Matching Pursuit (OMP). The quality of reconstructed speech signal is quantitatively expressed using different metrics like Mean Square Error (MSE), Segmental Signal to Noise Ratio (SSNR) and Perceptual Evaluation of Speech Quality (PESQ). For a 60 percentage of samples, the value of MSE obtained by using a combination of sparsifying basis DCT and reconstruction algorithm BP is 0.00018122. Using the same conditions the value of SSNR and PESQ is found to bell.3 dB and 2.574 respectively.