Study of Differential Evolutionary Algorithm in Blind Source Separation

Abstract

Blind source separation is a well known problem that arises in a large number of signal processing applications. In this paper we proposed a novel Evolutionary algorithm for Blind source separation of Instantaneous mixtures for optimization of continuous time domain signals. Among various evolutionary optimization principles, a population-based real-parameter optimization technique based on differences among population members is getting popular in various real-life optimization problems. This paper addresses this so-called Differential Evolution strategy and shows some sample cases where it can be utilized to separate a number of source signals using a particular channel.

Reference

• A.K. Qin, V. L. Huang, and P. N. Suganthan “Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization” IEEE Trans. On Evolutionary Computation 1,2008.
• A. K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” in Proc. IEEE Congr. Evolut.Comput., Edinburgh, Scotland, Sep. 2005, pp. 1785–1791.
• Hyvarinen, et al., “Independent Component Analysis”, John Wiley & Sons Co, 2001.
• Hyvärinen and E. Oja, “Independent Component Analysis:Algorithms and Applications”, Neural Networks, vol 13, pp. 411-430, 2000.
• Andrzej Cichocki and Frank Ehlers “Advances in Blind Source Separation” EURASIP Journal on Advances in Signal Processing Volume 2007
• Cardoso, J. F., (1999), “High-order Contrasts for IndependentComponent Analysis”, Neural Computation, Vol. 11(1), pp. 157-192.
• F. Yin, J. Wang, and C. Guo (Eds.): “Frequency-Domain Separation Algorithms for Instantaneous Mixtures” Springer-Verlag Berlin Heidelberg 2004, pp. 666–671, 2004.
• M. Kadou, K. Arakawa, “A Method of Blind Source Separation for Mixed Voice Separation in Noisy and Reverberating Environment”, IEICE Tech. Rep., vol. 108, no. 461, SIS2008-81, pp. 55- 59, March 2009.
• Storn, R. and Price, K., (1997), “Differential Evolution –A Simple and Efficient Heuristic for Global Optimizationover Continuous Spaces”, Journal of Global Optimization,Vol. 11, pp. 341-359.
• S. C. Douglas, H. Sawada, and S. Makino, “Natural gradient multichannelblind deconvolution and speech separation using causal FIR filters,” IEEE Trans. Speech Audio Process., vol. 13, pp. 92–104, Jan.2005.
• Shuxue Ding, Member, IEEE, Jie Huang, Member, IEEE, Daming Wei, Member, IEEE, and Andrzej Cichocki, Member, IEEE “A Near Real-Time Approach for Convolutive Blind Source Separation” IEEE Trans. On Circuits and systems—I: Regular Papers, vol.53, no. 1, January 2006
• Y. Gao, S. Xie, “A blind source separation algorithm using particle swarm optimization”, Proceedings of the IEEE 6th Circuits and Systems Symposium, Vol. 1, Issue , pp. 297 - 300 Vol.1, 31 May-2 June 2004.
• Yan Wanga,_, Xiao-Yue Fenga, Yan-Xin Huanga, Dong-Bing Pub, Wen-Gang Zhoua,Yan-Chun Lianga, Chun-Guang Zhoua “ A novel quantum swarm evolutionary algorithm and its applications” Neurocomputing 70 (2007) 633–640
• Yan Li', Peng Wen' and David Powers' “METHODS FOR THE BLIND SIGNAL SEPARATION PROBLEM” IEEE Int. Conf. Neural Networks & Signal Processing Nanjing, China, December 14-17.2003
• Z. Shi, Z. Jiang and F. Zhou, “A fixed-point algorithm for blind source separation with nonlinear autocorrelation,” Journal of Computational and Applied Mathematics, 223 908–915, 2009.

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