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Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning
Joint Authors
Wang, Zhe
Wang, Luyun
Li, Xiumei
Zhao, Lifan
Bi, Guoan
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-17
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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 K-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.
American Psychological Association (APA)
Wang, Zhe& Wang, Luyun& Li, Xiumei& Zhao, Lifan& Bi, Guoan. 2016. Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1112128
Modern Language Association (MLA)
Wang, Zhe…[et al.]. Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning. Mathematical Problems in Engineering No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1112128
American Medical Association (AMA)
Wang, Zhe& Wang, Luyun& Li, Xiumei& Zhao, Lifan& Bi, Guoan. Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1112128
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112128