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Volume 31 Issue 10
Dec.  2010
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He Jin, Liu Zhong. Screened-Ratio-Principle-Based DOA Estimation Algorithm in Impulsive Noise Environment[J]. Journal of Electronics & Information Technology, 2006, 28(5): 875-878.
Citation: Xu Xian-feng, Feng Da-zheng. A New Method Based on a Multi-Stage Algorithm for Blind Source Separation of Convolutive Mixtures[J]. Journal of Electronics & Information Technology, 2009, 31(10): 2455-2459. doi: 10.3724/SP.J.1146.2008.01427

A New Method Based on a Multi-Stage Algorithm for Blind Source Separation of Convolutive Mixtures

doi: 10.3724/SP.J.1146.2008.01427
  • Received Date: 2008-11-03
  • Rev Recd Date: 2009-03-23
  • Publish Date: 2009-10-19
  • A time-domain Multi-Stage Algorithm (MSA) based on the second order statistics for blind source separation of convolutive mixtures is proposed. Whitening procedure is adopted to transform the mixing matrix into a unitary matrix. The unitary matrix is expressed as a column-block matrix according to the block-diagonalization structure in autocorrelation matrices of source signals at different time delays. A novel least square tri-quadratic cost function with respect to a certain column block of the unitary matrix is proposed utilizing the orthogonality between each two different column blocks. Furthermore, a regular Triply Iterative Algorithm (TIA) following the gradient descent idea is used to seek the minimum point of the tri-quadratic cost function by alternately estimating one of the three independent variables parameter subsets, obtaining a column block of the unitary matrix. With each column block being got by using the systemic multi-stage algorithm, the unitary matrix can be estimated and then the source signals can be retrieved. Simulations results illustrate that, the new method outperforms the classic SUB method and the recently proposed JBD-NonU method, and can be efficiently applied to the blind source separation of convolutive mixtures.
  • Belouchrani A, Abed-Meraim K, and Cardoso J F, et al.. Ablind source separation technique using second-orderstatistics [J].IEEE Transactions on Signal Processing.1997,45(2):434-444[2]Feng D Z, Zhang X D, and Bao Z. An efficient multistagedecomposition approach for independent components [J].Signal Processing.2003, 83(1):181-197[3]Laar J, Moonen M, and Sommen P C W. MIMOinstantaneous blind identification based on second-ordertemporal structure [J].IEEE Transactions on SignalProcessing.2008, 56(9):4354-4364[4]Feng D Z, Zheng W X, and Cichocki A. Matrix-groupalgorithm via improved whitening process for extractingstatistically independent sources from array signals [J].IEEETransactions on Signal Processing.2007, 55(3):962-977[5]Buchner H, Aichner R, and Kellermann W. A generalizationof blind source separation algorithms for convolutivemixtures based on second-order statistics [J].IEEETransactions on Speech and Audio Processing.2005, 13(1):120-134[6]Gorokhov A and Loubaton P. Subspace-based techniques forblind separation of convolutive mixtures with temporallycorrelated sources [J].IEEE Transactions on Circuits andSystems.1997, 44(9):813-820[7]Ghennioui H, Fadaili E M, and Moreau N T, et al.. Anonunitary joint block diagonalization algorithm for blindseparation of convolutive mixtures of sources [J].IEEE SignalProcessing Letters.2007, 14(11):860-863[8]Sawada H, Mukai R, and Araki S, et al.. A robust and precisemethod for solving the permutation problem offrequency-domain blind source separation [J].IEEETransactions on Speech and Audio Processing.2004, 12(5):530-538[9]He Z S, Xie S L, and Ding S X, et al.. Convolutive blindsource separation in the frequency domain based on sparserepresentation [J].IEEE Transactions on Audio, Speech, andLanguage Processing.2007, 15(5):1551-1563[10]Castella M, Rhioui S, and Moreau E, et al.. Quadratic higherorder criteria for iterative blind separation of a MIMOconvolutive mixture of sources [J].IEEE Transactions onSignal Processing.2007, 55(1):218-232
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