Research on Single-channel Blind Deconvolution Algorithm for Multi-source Signals

被引：2

作者：

Liu Ting ^{[1
]}

Yin Tiantian ^{[1
]}

Gong Zhenying ^{[1
]}

Guo Yina ^{[1
]}

机构：

[1] Taiyuan Univ Sci & Technol, Elect & Commun Engn, Taiyuan 030024, Peoples R China

来源：

JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY | 2022年 / 44卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Blind source seperation; Single-channel blind deconvolution; Multi-source separation; Generative adversarial networks; Mixing matrix estimation; SELF-RECOVERING EQUALIZATION;

D O I：

10.11999/JEIT200933

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Traditional single-channel blind deconvolution method has the limitation that it can only separate two sources from a mixture. Considering this problem, a Single-Channel Blind Deconvolution algorithm based on optimized deep Convolutional generative adversarial networks (SCBDC) is proposed to separate and deconvolve more than three independent sources and mixing matrix only from a mixture. The experiments are carried on the occlusion Chinese character image datasets, four sources are randomly selected to be mixed with mixing matrix. Peak Signal to Noise Ratio (PSNR) and signal correlation index are combined to evaluate the separation effect. The result shows that the multiple sources can be effectively separated and deconvolved.

引用

页码：230 / 236

页数：7

共 18 条

[1] Color Balance and Fusion for Underwater Image Enhancement
Ancuti, Codruta O.
Ancuti, Cosmin
De Vleeschouwer, Christophe
Bekaert, Philippe
[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 27 (01) : 379 - 393
[2] CICHOCKI A, 2009, NONNEGATIVE MATRIX T, P132
[3] Coutu N., 2013, P IEEE SOUTH JACKS U, P1, DOI [10.1109/SECON.2013.6567408, DOI 10.1109/SECON.2013.6567408]
[4] [付卫红 Fu Weihong], 2017, [北京邮电大学学报, Journal of Beijing University of Posts Telecommunications], V40, P1
[5] SELF-RECOVERING EQUALIZATION AND CARRIER TRACKING IN TWO-DIMENSIONAL DATA COMMUNICATION-SYSTEMS
GODARD, DN
[J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 1980, 28 (11) : 1867 - 1875
[6] Two-Stage Single-Channel Audio Source Separation Using Deep Neural Networks
Grais, Emad M.
Roma, Gerard
Simpson, Andrew J. R.
Plumbley, Mark D.
[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2017, 25 (09) : 1469 - 1479
[7] Huaping JIA, 2011, INFORM TECHNOLOGY, P38, DOI [10.3969/j.issn.1009-2552.2011.05.013, DOI 10.3969/J.ISSN.1009-2552.2011.05.013.JIA]
[8] Kong QQ, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2747
[9] Single Channel speech separation based on empirical mode decomposition and Hilbert Transform
Krishna, Prasanna Kumar Mundodu
Ramaswamy, Kumaraswamy
[J]. IET SIGNAL PROCESSING, 2017, 11 (05) : 579 - 586
[10] Learning the parts of objects by non-negative matrix factorization
Lee, DD
Seung, HS
[J]. NATURE, 1999, 401 (6755) : 788 - 791

← 1 2 →