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Volume 30 Issue 3
Dec.  2010
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Xu Shun, Liu Yu-lin, Chen Shao-rong . A Time-Domain Algorithm for Blind Source Separation of Non-stationary Convolutive Mixtures[J]. Journal of Electronics & Information Technology, 2008, 30(3): 589-592. doi: 10.3724/SP.J.1146.2006.01273
Citation: Xu Shun, Liu Yu-lin, Chen Shao-rong . A Time-Domain Algorithm for Blind Source Separation of Non-stationary Convolutive Mixtures[J]. Journal of Electronics & Information Technology, 2008, 30(3): 589-592. doi: 10.3724/SP.J.1146.2006.01273

A Time-Domain Algorithm for Blind Source Separation of Non-stationary Convolutive Mixtures

doi: 10.3724/SP.J.1146.2006.01273 cstr: 32379.14.SP.J.1146.2006.01273
  • Received Date: 2006-08-28
  • Rev Recd Date: 2007-01-30
  • Publish Date: 2008-03-19
  • In this paper, a time-domain blind source separation algorithm for non-stationary convolutive mixtures is proposed by reprogramming the vectors of convolutive mixture model and generalizing the joint approximate diagonalization method. Firstly the sampling convolutive mixture signals are reseted for matching instantaneous mixture model, then considering non-stationarity of the sources, space whitening and joint block-diagonalization method is exploited to obtain the original signals. This algorithm simplifies the convolutive mixture problem into the instantaneous mixture problem from a new point of view, so it avoids domain transformation and convolution operation, as well as decreases the complexity. Computer simulation verifies its effectiveness and gives the analysis results about the effect on the signal to interference ratio as its parameter changes.
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  • Cichocki A and Amari S. Adaptive Blind Signal and ImageProcessing: Learning Algorithm and Appilication [M].England: John Wiley, 2002, Chapter 1.[2]Crucez-Alvarez S A, Cichocki A, and Amari S. From blindsignal extraction to blind instantaneous signal separation:criteria, algorithms and stability[J]. IEEE Trans. on NeuralNetworks, 2004, 15(4): 859-873.[3]Makino S, Sawada H. Mukai R, and Araki S, Blind sourceseparation of convolutive mixtures of speech in frequencydomain[J].IEICE Trans. Fundamentals.2005, E88-A(7):1640-1655[4]Araki S, Mukai R, Makino S, Nishikawa T, and SaruwatariH. The fundamental limitation of frequency domain blindsource separation for convolutive mixtures of speech[J].IEEETrans. on Speech Audio Processing.2003, 11(2):109-116[5]Georgiev P G, Theis F, and Cichocki A. Sparse componentanalysis and blind source separation of underdeterminedmixtures[J].IEEE Trans. on Neural Networks.2005, 16(4):992-996[6]赵彩华, 刘琚, 孙建德, 闫华. 基于小波变换和独立分量分析的含噪混叠语音盲分离[J].电子与信息学报.2006, 28(9):1565-1568浏览[7]Cardoso J F. Multidimensional independent componentanalysis[A]. In Proc. ICASSP, Seattle, 1998: 1941-1944.[8]Choi S, Cichocki A, Park H M, and Lee S Y. Blind sourceseparation and independent component analysis: A review[J].Neural Information Processing-Letters and Reviews, 2005,6(1): 1-57.[9]Cichicki A and Unbehauen R. Neural Networks forOptimization and Signal Processing[M]. John Wiley Sons,New York, 1994, chapter 5.
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