Abstract
In this paper, a hybrid genetic algorithm for blind signal separation that extracts the individual unknown independent source signals out of given linear signal mixture is presented. The proposed method combines a genetic algorithm with local search and is called the hybrid genetic algorithm. The implemented separation method is based on evolutionary minimization of the separated signal cross-correlation. The convergence behaviour of the network is demonstrated by presenting experimental separating signal results. A computer simulation example is given to demonstrate the effectiveness of the proposed method. The hybrid genetic algorithm blind signal separation performance is better than the genetic algorithm at directly minimizing the Kullback-Leibler divergence. Eventually, it is hopeful that this optimization approach can be helpful for blind signal separation engineers as a simple, useful and reasonable alternative.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Comon, P., Jutten, C., Herault, J.: Blind separation of sources, Part II: Problem statements. Signal Processing 24, 11–20 (1991)
Comon, P.: Independent component analysis - A new concept. Signal Processing 36, 287–314 (1994)
Fren, M., Kammeyer, K.: Application of source separation slgorithms for mobile communication environment. In: 1st International Conference Workshop on ICA & Signal Separation, Aussois, France, pp. 431–436 (1999)
Girolami, M.: Noise reduction and speech enhancement via temporal anti-hebbian learning. In: ICASSP, vol. 2, pp. 1233–1236 (1998)
Choi, S., Cichocki, A.: Adaptive blind separation of speech signals: Cocktail party problem. In: International Conference of Speech Processing, Seoul, Korea, pp. 6117–6221 (1997)
Yen, K., Zhao, Y.: Co-channel speech separation for robust automatic speech recognition. In: ICASSP, vol. 2, pp. 859–862 (1997)
Buchner, H., Aichner, R., KellermannH, W.: A Generalization of a Class of Blind Source Separation Algorithms for Convolutive Mixtures. In: Proc. IEEE Int. Symposium on Independent Component Analysis and Blind Signal Separation, pp. 945–950 (2003)
Koutras, A., Dermatas, E., Kokkinakis, G.: Blind signal separation and speech recognition in the frequency domain. In: 6th International Conference on Electronics, Circuits and Systems, vol. 1, pp. 427–430 (1999)
Koutras, A., Dermatas, E., Kokkinakis, G.: Recognizing simultaneous speech: A genetic algorithm approach. In: Euro-Speech, Budapest, vol. 6, pp. 2551–2554 (1999)
Chen, Y.C., Shyr, W.J., Su, T.J., Wang, B.W.: Memetic algorithm for adaptive infinite impulse response filtering. In: Proceeding of the 2002 IEEE International Symposium on Intelligent Signal Processing and Communication Systems, pp. 1–5 (2002)
Freisleben, B., Merz, P.: A genetic local search algorithm for solving symmetric and asymmetric travelling salesman problems. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 616–621. IEEE Press, Los Alamitos (1996)
Burke, E.K., Smith, A.J.: Hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Transactions on Power Systems 15(1), 122–128 (2000)
Lambert, R.: Multi channel blind deconvolution: FIR matrix algebra and separation of multipath mixtures, Ph.D Thesis, University of Southern California, Dept.of Electrical Engineering (1996)
Ishibuchi, H., Murata, T.: Multi-objective genetic local search algorithm. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 119–124. IEEE Press, Los Alamitos (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shyr, WJ. (2006). The Hybrid Genetic Algorithm for Blind Signal Separation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_105
Download citation
DOI: https://doi.org/10.1007/11893295_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)