Underdetermined Blind Source Separation Using Sparse Coding

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 m . n. By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 28; číslo 12; s. 3102 - 3108
Hlavní autoři: Zhen, Liangli, Peng, Dezhong, Yi, Zhang, Xiang, Yong, Chen, Peng
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Blind source separation
Clustering
Clustering algorithms
Coding
Encoding
Estimation
Learning systems
Mixing matrix identification
Representations
Separation
Signal processing
single source detection
source recovery
sparse coding
Sparse matrices
Subspaces
Theoretical analysis
Time-frequency analysis
underdetermined blind source separation (UBSS)
ISSN:2162-237X, 2162-2388
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Shrnutí: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 m . n. 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.
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ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2016.2610960