A heuristic approach for blind source separation of instant mixtures

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.

[1]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[2]  S. Lehrer.  Understanding Lung Sounds , 2002 .

[3]  Jean-Luc Starck,et al.  Blind Component Separation in Wavelet Space: Application to CMB Analysis , 2005, EURASIP J. Adv. Signal Process..

[4]  Christopher J. James.  Introduction and overview of the BSS/ICA problem - specifically when applied to biomedicine , 2004 .

[5]  Mercedes Atienza,et al.  Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis , 2008, Annals of Biomedical Engineering.

[6]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[7]  Z. Moussavi,et al.  Heart Sounds Separation From Lung Sounds Using Independent Component Analysis , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[8]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[9]  Lang Tong,et al.  Indeterminacy and identifiability of blind identification , 1991 .

[10]  Antonio J. Nebro,et al.  Metaheurísticas multiobjetivo adaptativas , 2013, Computación y Sistemas.

[11]  C. Fancourt,et al.  The coherence function in blind source separation of convolutive mixtures of non-stationary signals , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[12]  Christian W. Hesse,et al.  Model order estimation for blind source separation of multichannel magnetoencephalogram and electroencephalogram signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Miguel Angel Mañanas,et al.  A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case , 2008, Comput. Biol. Medicine.

[14]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[15]  Arie Yeredor,et al.  Multiple-snapshots BSS with general covariance structures: A partial maximum likelihood approach involving weighted joint diagonalization , 2012, Signal Process..

[16]  Christian Jutten,et al.  Blind Extraction of Smooth Signals Based on a Second-Order Frequency Identification Algorithm , 2010, IEEE Signal Processing Letters.

[17]  Hichem Snoussi,et al.  Blind separation of noisy Gaussian stationary sources. Application to cosmic microwave background imaging , 2002, 2002 11th European Signal Processing Conference.

[18]  Pierre Comon,et al.  Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .

[19]  Olivier Sibony,et al.  Fetal electrocardiogram extraction based on non-stationary ICA and wavelet denoising , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[20]  Janusz Jezewski,et al.  Determination of fetal heart rate from abdominal signals: evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram , 2012, Biomedizinische Technik. Biomedical engineering.

[21]  Hugo Vélez-Pérez,et al.  Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling , 2012, Biomed. Signal Process. Control..

[22]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[23]  G. C. Carter,et al.  Estimation of the Magnitude-Squared Coherence Function (Spectrum) , 1972 .

[24]  Trevor Gibbs.  Auscultation Skills: Breath and Heart Sounds , 2000, BMJ : British Medical Journal.

[25]  A. Cichocki,et al.  Blind separation of nonstationary sources in noisy mixtures , 2000 .

[26]  G. Clifford Carter,et al.  Coherence and time delay estimation : an applied tutorial for research, development, test, and evaluation engineers , 1993 .

[27]  Sergio Cuenca-Asensi,et al.  Aplicación de lógica difusa para el particionado hardware/software en sistemas embebidos , 2013, Computación y Sistemas.

[28]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[29]  G. Darmois,et al.  Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaire , 1953 .

[30]  Akane Sano,et al.  Nonlinear blind source separation using coherence function , 2003, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).