Speech compression using compressive sensing on a multicore system

Abstract

Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals, i.e. speech signal. Compressive sensing is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. In this paper, a novel algorithm for speech coding utilizing CS principle is developed. The sparsity of speech signals is exploited using gammatone filterbank and Discrete Cosine Transform (DCT) in which the compressive sensing principle is then applied to the sparse subband signals. All parameters will be optimized using informal listening test and Perceptual Evaluation of Speech Quality (PESQ). In order to further reduce the bit requirement, vector quantization using codebook of the training signals will be added to the system. The performance of overall algorithms will be evaluated based on the processing time and speech quality. Finally, to speed up the process, the proposed algorithm will be implemented in a multicore system, i.e. six cores, using Single Program Multiple Data (SPMD) parallel paradigm.