CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
Random Articles
Reseach Article

Study of Differential Evolutionary Algorithm in Blind Source Separation

by Monorama Swain, Rutuparna Panda, Sneha Tibrewal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 30 - Number 7
Year of Publication: 2011
Authors: Monorama Swain, Rutuparna Panda, Sneha Tibrewal
10.5120/3650-5102

Monorama Swain, Rutuparna Panda, Sneha Tibrewal . Study of Differential Evolutionary Algorithm in Blind Source Separation. International Journal of Computer Applications. 30, 7 ( September 2011), 48-55. DOI=10.5120/3650-5102

@article{ 10.5120/3650-5102,
author = { Monorama Swain, Rutuparna Panda, Sneha Tibrewal },
title = { Study of Differential Evolutionary Algorithm in Blind Source Separation },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 30 },
number = { 7 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 48-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume30/number7/3650-5102/ },
doi = { 10.5120/3650-5102 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:26.588071+05:30
%A Monorama Swain
%A Rutuparna Panda
%A Sneha Tibrewal
%T Study of Differential Evolutionary Algorithm in Blind Source Separation
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 7
%P 48-55
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
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.

References
  1. 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.
  2. 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.
  3. Hyvarinen, et al., “Independent Component Analysis”, John Wiley & Sons Co, 2001.
  4. Hyvärinen and E. Oja, “Independent Component Analysis:Algorithms and Applications”, Neural Networks, vol 13, pp. 411-430, 2000.
  5. Andrzej Cichocki and Frank Ehlers “Advances in Blind Source Separation” EURASIP Journal on Advances in Signal Processing Volume 2007
  6. Cardoso, J. F., (1999), “High-order Contrasts for IndependentComponent Analysis”, Neural Computation, Vol. 11(1), pp. 157-192.
  7. F. Yin, J. Wang, and C. Guo (Eds.): “Frequency-Domain Separation Algorithms for Instantaneous Mixtures” Springer-Verlag Berlin Heidelberg 2004, pp. 666–671, 2004.
  8. 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.
  9. 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.
  10. 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.
  11. 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
  12. 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.
  13. 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
  14. 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
  15. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Blind source separation Instantaneous mixture Differential evolution

Powered by PhDFocusTM