Underdetermined blind source separation using sparse coding

Zhen, L, Peng, D, Yi, Z, Xiang, Yong and Chen, P 2017, Underdetermined blind source separation using sparse coding, IEEE transactions on neural networks and learning systems, vol. 28, no. 12, pp. 3102-3108, doi: 10.1109/TNNLS.2016.2610960.

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Title Underdetermined blind source separation using sparse coding
Author(s) Zhen, L
Peng, D
Yi, Z
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Chen, P
Journal name IEEE transactions on neural networks and learning systems
Volume number 28
Issue number 12
Start page 3102
End page 3108
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017
ISSN 2162-237X
Keyword(s) Mixing matrix identification
single source detection
source recovery
sparse coding
underdetermined blind source separation (UBSS)
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Summary In an underdetermined mixture system with n unknown sources, it is a challenging task to separate these sources from their m observed mixture signals, where . By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency (TF) representation vectors of observed mixture signals. We show that these 1-D subspaces are associated with TF points where only single source possesses dominant energy. By grouping the vectors in these subspaces via hierarchical clustering algorithm, we obtain the estimation of the mixing matrix. Finally, the source signals could be recovered by solving a series of least squares problems. Since the sparse coding strategy considers the linear representation relations among all the TF representation vectors of mixing signals, the proposed algorithm can provide an accurate estimation of the mixing matrix and is robust to the noises compared with the existing underdetermined blind source separation approaches. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.
Language eng
DOI 10.1109/TNNLS.2016.2610960
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30101791

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