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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.18 n.4 México Oct./Dec. 2014 

Artículos regulares


A Heuristic Approach for Blind Source Separation of Instant Mixtures


Jesús Rigoberto Villavicencio Navarro, Luis Márquez Martínez, and Joaquín Álvarez Gallegos


CICESE Research Center, Electronics and Telecommunications Department, Ensenada, B.C., Mexico.,,


Article received on 13/03/2014.
Accepted on 08/05/2014.



In this paper we present a methodology for blind source separation (BSS) based on a coherence function to solve the problem of linear instantaneous mixtures of signals. The proposed methodology consists of minimizing the coherence function using a heuristic algorithm based on the simulating annealing method. Also, we derived an analytical expression of the coherence for the BSS model, in which it is found that independent and identically distributed (iid) Gaussian components can be recovered. Our results show satisfactory performance in comparison with traditional methods.

Keywords: Blind source separation, second-order statistics, source extraction, Gaussian sources, simulated annealing.





1. Belouchrani, A., Abed-Meraim, K., Cardoso, J.F. & Moulines, E. (1997). A blind source separation technique using second-order statistics, IEEE Transactions on Signal Processing, Vol. 45, No. 2: pp. 434-444.         [ Links ]

2. Cardoso, J.F., Snoussi, H., Delabrouille, J. & Patanchon, G. (2002). Blind separation of noisy Gaussian stationary sources. Application to cosmic microwave background imaging. ArXiv Astrophysics e-prints.         [ Links ]

3. Cardoso, J., & Souloumiac, A. (1993). Blind beamforming for non-gaussian signals. Radar and Signal Processing, IEE Proceedings F, Vol. 140, No. 6, pp. 362-370.         [ Links ]

4. Carter, G.C. (1972). Estimation of the magnitude-squared coherence function (spectrum). Tech. Rep. AD0743945, Naval underwater systems center Newport RI, New London, Conn.         [ Links ]

5. Carter, G.C. (1993). Coherence and time delay estimation an applied tutorial for research, development, test, and evaluation engineers. IEEE Press.         [ Links ]

6. Cichocki, A., & Amari, S. (2003). Adaptive Blind Signal and Image Processing. John Wiley and Sons, Ltd.         [ Links ]

7. Cichocki, A., Amari, S., Siwek, K., Tanaka, T. & Anh Huy Phan. ICALAB Toolboxes [online]         [ Links ].

8. Choi, S., & Cichocki, A. (2000). Blind separation of nonstationary sources in noisy mixtures. Electronics Letters, Vol. 36, No. 9, pp. 848-849.         [ Links ]

9. Comon, P. & Jutten, C. (2010). Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press.         [ Links ]

10. Corporation, S. (2002), Auscultation skills: breath & heart sounds. Springhouse.         [ Links ]

11. Crespo-Garcia, M., Atienza, M., & Cantero, J.L. (2008). Muscle artifact removal from human sleep EEG by using independent component analysis. Annals of biomedical engineering, Vol. 36, No. 3, pp. 467-475.         [ Links ]

12. Darmois, G. (1953). Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaire. Revue de l'Instituí International de Statistique / Review of the International Statistical Institute, Vol. 21, No. 1/2, pp. 2-8.         [ Links ]

13. Díaz Pando, H., Cuenca Asensi, S., Sepúlveda Lima, R., Fajardo Calderín, J., & Rosete Suárez, A. (2013). Aplicación de lógica difusa para el particionado hardware/software en sistemas embebidos. Computación y Sistemas, Vol. 17, No. 1, pp. 25-39.         [ Links ]

14. Duarte, L., Rivet, B., & Jutten, C. (2010). Blind extraction of smooth signals based on a second-order frequency identification algorithm. Signal Processing Letters IEEE, Vol. 17, No. 1, pp. 79-82        [ Links ]

15. Fancourt, C. & Parra, L. (2001). The coherence function in blind source separation of convolutive mixtures of non-stationary signals. Neural Networks for Signal Processing XI, Proceedings of the IEEE Signal Processing Society Workshop, pp. 303-312.         [ Links ]

16. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.Ch., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet, Components of a New Research Resource for Complex Physiologic Signals. Circulation, Vol. 101, No. 23, pp. e215-e220.         [ Links ]

17. Hesse, C.W. (2008). Model order estimation for blind source separation of multichannel magnetoencephalogram and electroencephalogram signals. Engineering in Medicine and Biology Society, EMBS 2008, 30th Annual International Conference of the IEEE, pp.3348-3351, IEEE.         [ Links ]

18. Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, Vol. 10, No. 3, pp. 626-634.         [ Links ]

19. Hyvärinen, A. & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, Vol. 13, No. 4-5, pp. 411-430.         [ Links ]

20. James, C.J. (2004). Introduction and overview of the BSS/ICA problem-specifically when applied to biomedicine. IEEE Seminar on Blind Source Separation in Biomedicine.         [ Links ]

21. Jezewski, J., Matonia, A., Kupka, T., Roj, D. & Czabanski, R. (2012). Determination of the fetal heart rate from abdominal signals: evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram. Biomedical Engineering/ Biomedizinische Technik, Vol. 57, No. 5, pp. 383-394.         [ Links ]

22. Kirkpatrick, S., C.D. Gelatt, & M.P. Vecchi (1983). Optimization by simulated annealing. Science, Vol. 220, No. 4598, pp. 671-680.         [ Links ]

23. Lehrer, S. (2002). Understanding lung sounds. W.B. Saunders.         [ Links ]

24. Machin Navas, M., & Nebro Urbaneja, A.J. (2013). Metaheurísticas multiobjetivo adaptativas. Computación y Sistemas, Vol. 17, No. 1, pp. 53-62.         [ Links ]

25. Moudden, Y., Cardoso, J.F., Starck, J.L. & Delabrouille, J. (2005). Blind component separation in wavelet space: application to cmb analysis. EURASIP J. Appl. Signal Process., Vol. 2005, pp. 2437-2454.         [ Links ]

26. Oku,T. & Sano, A. (2003). Nonlinear blind source separation using coherence function. SICE 2003 Annual Conference, pp. 2550-2560.         [ Links ]

27. Pourazad, M.T., Moussavi, Z., Farahmand, F. & Ward, R.K. (2005). Heart Sounds Separation From Lung Sounds Using Independent Component Analysis. Engineering in Medicine and Biology Society, 27th Annual International Conference of the IEEE-EMBS, pp. 2736-2739.         [ Links ]

28. Romero, S., Mañanas, M.A., & Barbanoj, M.J. (2008). A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case. Computers in biology and medicine, Vol. 38, No. 3, pp. 348-360.         [ Links ]

29. Romo-Vázquez, R., Vélez-Pérez, H., Ranta, R., Louis Dorr, V., Maquin, D., & Maillard, L. (2012). Blind source separation, wavelet denoising and discriminant analysis for eeg artefacts and noise cancelling. Biomedical Signal Processing and Control, Vol. 7, No. 4, pp. 389-400.         [ Links ]

30. Tong, L., Liu, R.W., Soon, V., & Huang, Y.F. (1991). Indeterminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems, Vol. 38, No. 5, pp. 499-509.         [ Links ]

31. Vigneron, V., Paraschiv-Ionescu, A., Azancot, A., Sibony, O., & Jutten, C. (2003). Fetal electrocardiogram extraction based on non-stationary ICA and wavelet denoising. Proceedings. Seventh International Symposium on Signal Processing and Its Applications, IEEE, Vol. 2, pp. 69-72.         [ Links ]

32. Yeredor, A. (2012). Multiple-snapshots bss with general covariance structures: A partial maximum likelihood approach involving weighted joint diagonalization. Signal Processing, Vol. 92, No. 8, pp. 1832-1843.         [ Links ]

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