Speech Coding Based on Compressed Sensing and Sparse Representation

• Published in: Applied Mechanics and Materials Vol. 667; no. Advances in Computers, Electronics and Mechatronics; pp. 242 - 247
• Main Authors: Li, Shang Jing, Zhu, Qi
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.10.2014
• Subjects: Approximation; Coding; Detection; Linear prediction; Low pass filters; Representations; Speech; Synthesis; Speech Coding; Measurement Modeling; Sparse Representation; Compressed Sensing
• ISBN: 9783038352907; 303835290X
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.667.242

In this paper, we propose a novel speech coding scheme based on compressed sensing and sparse representation. Compressed sensing (CS) attracts great interest for its ability to utilize a few measurements to recover original signals. Measurements preserve part of speech features while projected by row echelon matrix. A dictionary is learned in order to contain redundant information about speech measurements. The synthesized speech is recovered from a sparse approximation of the corresponding measurement. A rear low-pass filter is adopted to improve the subject quality of synthesized speech. Results show that the proposed coding scheme has achieved average Mean Opinion Score (MOS) of the synthesized speech 3.083 in an appropriate bit rate (4.2 Kbps), which outperforms the quality of Code excited linear prediction (CELP).