Novel Single Channel Blind Source Separation Algorithm Based on Sparse Representation

TIAN Yuanrong; WANG Xing; ZHOU Yipeng

doi:10.11999/JEIT160888

Volume 39 Issue 6

Jun. 2017

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TIAN Yuanrong, WANG Xing, ZHOU Yipeng. Novel Single Channel Blind Source Separation Algorithm Based on Sparse Representation[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1371-1378. doi: 10.11999/JEIT160888

Citation:

TIAN Yuanrong, WANG Xing, ZHOU Yipeng. Novel Single Channel Blind Source Separation Algorithm Based on Sparse Representation[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1371-1378. doi: 10.11999/JEIT160888

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Novel Single Channel Blind Source Separation Algorithm Based on Sparse Representation

doi: 10.11999/JEIT160888

Funds:

The National Natural Science Foundation of China (61372167), The Aviation Science Foundation of China (20152096019)

Received Date: 2016-09-02
Rev Recd Date: 2017-01-22
Publish Date: 2017-06-19

Abstract

Abstract

The main drawback of sparse representation based Single Channel Blind Source Separation (SCBSS) is the interference between sub-dictionaries. To alleviate this drawback, an extra sub-dictionary, named common sub-dictionary, is proposed to add into traditional union dictionary. The single source is reconstructed by linear combining sparsely activity atoms of its corresponding sub-dictionary and common sub-dictionary. The common sub-dictionary can pure discriminative information in each sources specified sub-dictionary since the common information different sources shared together is gathered in common sub-dictionary. The optimization of objective function involves three steps: sparse representation, dictionary updating and weight coefficients optimization, the three steps are iteratively performed for a specified number of times or until convergence. In test stage, single source separation is achieved by combining atoms in source corresponding sub-dictionary and common sub-dictionary with the sparse coefficients of single mixed signal over union dictionary. Experimental results on speech dataset show that, when compared with traditional and state of art algorithms, the proposed algorithm can improve the performance 1 dB at most.
- Sparse representation,
- Single channel blind source separation,
- Dictionary learning,
- Discrimination,
- Fidelity

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References(25)

References

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