Frequency Domain Blind Source Separation Permutation Algorithm Based on Regional Growth Correction

Tianqi ZHANG; Huawei ZHANG; Donghua LIU; Qun LI

doi:10.11999/JEIT180386

Volume 41 Issue 3

Mar. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(3): 580-587

Tianqi ZHANG, Huawei ZHANG, Donghua LIU, Qun LI. Frequency Domain Blind Source Separation Permutation Algorithm Based on Regional Growth Correction[J]. Journal of Electronics & Information Technology, 2019, 41(3): 580-587. doi: 10.11999/JEIT180386

Citation:

Tianqi ZHANG, Huawei ZHANG, Donghua LIU, Qun LI. Frequency Domain Blind Source Separation Permutation Algorithm Based on Regional Growth Correction[J]. Journal of Electronics & Information Technology, 2019, 41(3): 580-587. doi: 10.11999/JEIT180386

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Frequency Domain Blind Source Separation Permutation Algorithm Based on Regional Growth Correction

doi: 10.11999/JEIT180386

Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61671095, 61371164, 61702065, 61701067, 61771085), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), Chongqing Graduate Research and Innovation Project (CYS17219), The Research Project of Chongqing Educational Commission (KJ1600427, KJ1600429)

Received Date: 2018-04-25
Rev Recd Date: 2018-10-10

Available Online: 2018-10-24

Publish Date: 2019-03-01

Abstract

Abstract

The convolutive blind source separation can be effectively solved in frequency domain, but blind source separation in frequency domain must solve the problem of ranking ambiguity. A frequency-domain blind source separation sorting algorithm is proposed based on regional growth correction. First, the convolutional mixed signal short-time Fourier transform is used to establish an instantaneous model at each frequency point in the frequency domain for independent component analysis. Based on this, the correlation of the power ratio of the separated signal is used to sort all frequency points one by one replacement. Second, according to the threshold, the sorted result is divided into several small areas. Finally. regional replacement and merging is performed according to the regional growth method, and the correct separation signal is finally obtained. Regional growth correction minimizes the mis-proliferation of frequency sorting and improves separation results. The speech blind source separation experiments are performed in the simulated and real environments respectively. The results show the effectiveness of the proposed algorithm.
- Convolutive blind source separation,
- Frequency domain permutation,
- Region growing,
- Power ratio correlation

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References(14)

References

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