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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 3982360, 13 pages
Research Article

Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning

1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
2School of Electronic Engineering and Automation, City College of Dalian University of Technology, Dalian, China
3School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, China

Received 31 March 2016; Revised 1 September 2016; Accepted 19 September 2016

Academic Editor: Eric Feulvarch

Copyright © 2016 Zhe Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper describes a novel algorithm for underdetermined speech separation problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two steps. The unknown mixing matrix is firstly estimated from the speech mixtures in the transform domain by using -means clustering algorithm. In the second step, the speech sources are recovered based on an autocalibration sparse Bayesian learning algorithm for speech signal. Numerical experiments including the comparison with other sparse representation approaches are provided to show the achieved performance improvement.