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Complex Blind Source Separation

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Abstract

Blind source separation (BSS) techniques aim at recovering the original source signals from observed mixtures without a priori information. The bivariate empirical mode decomposition (BEMD) algorithm combined with complex independent component analysis by entropy bound minimization (ICA-EBM) technique is proposed as an alternative to separate convolutive mixtures of speech signals. The empirical mode decomposition (EMD) is a local self-adaptive decomposition method that allows analyzing data from non-stationary and/or nonlinear processes. Its principle is based on the sequential extraction of different amplitude and frequency modulation single-component contributions called intrinsic mode functions (IMFs). The BEMD is an extension of the EMD to complex-valued signals. First, the convolutive mixtures in the frequency domain are decomposed into a set of IMFs using the BEMD algorithm, and then, the complex ICA-EBM method is applied to extract the independent sources. The performance of the proposed approach is tested on real speech sounds chosen from available databases and compared to the results obtained via conventional frequency ICA and BEMD-ICA-based separation for convolutive mixtures. Simulation results show that the proposed method of BSS outperforms the BEMD-ICA separation technique for convolutive mixtures and conventional frequency ICA.

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References

  1. T. Adali, D.-V. Calhoun, Complex ICA of brain imaging data. IEEE Signal Process. Mag. 24(5), 136–139 (2007)

    Article  MATH  Google Scholar 

  2. A. Adib, E. Moreau, D. Aboutajdine, Source separation contrasts using a reference signal. IEEE Signal Process. Lett. 11(3), 312–315 (2004)

    Article  Google Scholar 

  3. M.-U.B. Altaf, T. Gautama, T. Tanaka, D.-P. Mandic, Rotation invariant complex empirical mode decomposition, in Proceeding of ICASSP07 (2007), pp. 1009–1012

  4. J. Anemüler, B. Kollmeier, Amplitude modulation decorrelation for convolutive blind source separation, in Proceeding of ICA (2000), pp. 215–220

  5. J. Anemüller, T.J. Sejnowski, S. Makeig, Complex independent component analysis of frequency-domain electroencephalographic data. Neural Netw. 16(9), 1311–1323 (2003)

    Article  Google Scholar 

  6. I. Bekkerman, J. Tabrikian, Target detection and localization using mimo radars and sonars. IEEE Trans. Signal Process. 54(10), 3873–3883 (2006)

    Article  Google Scholar 

  7. E. Bingham, A. Hyvärinen, A fast fixed-point algorithm for independent component analysis of complex valued signals. Int. J. Neural Syst. 10(1), 1–8 (2000)

    Article  Google Scholar 

  8. A.-O. Boudraa, J.-C. Cexus, EMD-based signal filtering. IEEE Trans. Instrum. Meas. 56(6), 2196–2202 (2007)

    Article  Google Scholar 

  9. J.-F. Cardoso, Higher-order contrasts for independent component analysis. Neural Comput. 11(1), 157–192 (1999)

    Article  MathSciNet  Google Scholar 

  10. M. Castella, J.-C. Pesquet, A.-P. Petropulu, A family of frequency and time domain contrasts for blind separation of convolutive mixtures of temporally dependent signals. IEEE Trans. Signal Process. 53(1), 107–120 (2005)

    Article  MathSciNet  Google Scholar 

  11. J.C. Cexus, A.O. Boudraa, Non-stationary signals analysis by Teager–Huang Transform (THT), in Proceeding of EUSIPCO (2006)

  12. R. Chai, G. Naik, T.-N. Nguyen, S. Ling, Y. Tran, A. Craig, H. Nguyen, Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system. IEEE J. Biomed. Health Inform. (2016). doi:10.1109/JBHI.2016.2532354

  13. H. Chunming, G. Huadong, W. Changlin, F. Dian, A novel method to reduce speckle in SAR images. Int J Remote Sens. 23(23), 5095–5101 (2002)

    Article  Google Scholar 

  14. M.-A. Colominas, G. Schlotthauer, M.-E. Torres, Improve complete ensemble EMD: a suitable tool for biomedical signal processing. Biomed. Signal Process Control. 14, 19–29 (2014)

    Article  Google Scholar 

  15. P. Comon, E. Moreau, Blind mimo equalization and joint diagonalization criteria, inProceeding of ICASSP (2001)

  16. S. Cruces-Alvarez, A. Cichocki, L. Castedo-Ribas, An iterative inversion approach to blind source separation. IEEE Trans. Neural Netw. Learn. Syst. 11(6), 1423–1437 (2000)

    Article  Google Scholar 

  17. L. De Lathauwer, B. De Moor, On the blind separation of non-circular sources, in Proceeding of EUSIPCO (2002)

  18. Y. Deville, Panorama des applications biomédicales des méthodes de séparation aveugle de sources, in Proceeding of GRETSI (2003), pp. 31–34

  19. L.-E. Di Persia, D.-H. Milone, Using multiple frequency bins for stabilization of FD-ICA algorithms. Signal Process. 119(c), 162–168 (2016)

    Article  Google Scholar 

  20. S.-C. Douglas, Fixed-point algorithms for the blind separation of arbitrary complex-valued non-Gaussian signal mixtures. EURASIP J. Adv. Signal Process. 2007(1), 83–83 (2007)

    MathSciNet  Google Scholar 

  21. L. Du, B. Wang, Y. Li, H. Liu, Robust classification scheme for airplane targets with low resolution radar based on EMD-CLEAN feature extraction method. IEEE Sens. J. 13(12), 4648–4662 (2013)

    Article  Google Scholar 

  22. D. Farina, C. Févotte, C. Doncarli, R. Merletti, Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals. IEEE Trans. Biomed. Eng. 51(9), 1555–1567 (2004)

    Article  Google Scholar 

  23. C. Févotte, R. Gribonval, E. Vincent, BSS EVAL toolbox user guide, IRISA (2005), http://www.irisa.fr/metiss/bss_eval

  24. P. Flandrin, G. Rilling, P. Gonçalves, Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004)

    Article  Google Scholar 

  25. G.-S. Fu, R. Phlypo, M. Anderson, X.-L. Li, T. Adali, Blind source separation by entropy rate minimization. IEEE Trans. Signal Process. 62(16), 4245–4255 (2014)

    Article  MathSciNet  Google Scholar 

  26. J. Guo, Y. Deng, A time-frequency algorithm for noisy ICA, in Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science ed. by F. Bian, Y. Xie (2016), pp. 357–365

  27. Y. Guo, S. Huang, Y. Li, G.-R. Naik, Edge effect elimination in single-mixture blind source separation. Circuits Syst Signal Process. 32(5), 2317–2334 (2013)

    Article  MathSciNet  Google Scholar 

  28. Y. Guo, G.-R. Naik, H. Nguyen, Single channel blind source separation based local mean decomposition for Biomedical applications, in Proceeding of 35th Annual International Conference of the IEEE Eng Med Biol Soc (2013) pp. 6812–6815

  29. N.-E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond. A 454(1971), 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  30. A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis (Wiley, New York, 2001)

    Book  Google Scholar 

  31. E.-T. Jaynes, Information theory and statistical mechanics. Phys. Rev. 106(4), 620–630 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  32. C. Jutten, M. Babaie-Zadeh, Source separation: principles, current advances and applications, in Proceeding of IAR Annual Meet (2006)

  33. C. Jutten, P. Comon, Séparation de sources-Tome 2 : au-delà de l’aveugle et applications, Chap. 13, ed. by Y. Deville. Hermès–Lavoisier (2007)

  34. A. Kachenoura, L. Albera, L. Senhadji, Blind source separation in biomedical engineering. IRBM 28(1), 20–34 (2007)

    Article  Google Scholar 

  35. K. Kokkinakis, V. Zarzoso, A.-K. Nandi, Blind separation of acoustic mixtures based on linear prediction analysis, in Proceeding of the 4th ICA2003 (2003), pp. 343–348

  36. A. Krause, M. Olson, The Basics of S-PLUS (Statistics and Computing), 4th edn. (Springer-Verlag, New York, 2005)

  37. X. Li, T. Adali, Independent component analysis by entropy bound minimization. IEEE Trans. Signal Process. 58(10), 5151–5164 (2010)

    Article  MathSciNet  Google Scholar 

  38. X.-L. Li, T. Adalı, Complex independent component analysis by entropy bound minimization. IEEE Trans. Circuits Syst. I Regul. Pap. 57(7), 1417–1430 (2010)

    Article  MathSciNet  Google Scholar 

  39. W. Li, H. Z. Yang, Blind source separation in underdetermined model based on local mean decomposition and AMUSE algorithm, in Proceeding of CCC (2014), pp. 7206–7211

  40. B. Mijović, M. De Vos, I. Gligorijević, J. Taelman, S. Van Huffel, Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Trans. Biomed. Eng. 57(9), 2188–2196 (2010)

    Article  Google Scholar 

  41. B. Mijovic, M. De Vos, I. Gligorijevic, S. Van Huffel, Combining EMD with ICA for extracting independent sources from single channel and two-channel data, in Proceeding of 32nd Annual International Conference of the IEEE EMBS (2010)

  42. G.-R. Naik, A.-H. Al-Timemy, H.-T. Nguyen, Transradial amputee gesture classification using an optimal number of sEMG sensors: an approach using ICA clustering. IEEE Trans. Neural Syst. Rehabil. Eng. 24(8), 837–846 (2016)

    Article  Google Scholar 

  43. G.-R. Naik, K.-K. Dinesh, Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures. Comput. Methods Biomech. Biomed. Eng. 14(12), 1105–1111 (2011)

    Article  Google Scholar 

  44. G.-R. Naik, K.-K. Dinesh, P. Marimuthu, Signal processing evaluation of myoelectric sensor placement in low-level gestures: sensitivity analysis using independent component analysis. Expert Syst. 31(1), 91–99 (2014)

    Article  Google Scholar 

  45. G.-R. Naik, S.-E. Selvan, H.-T. Nguyen, Single-channel EMG classification with ensemble empirical mode decomposition based ICA for diagnosing neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(7), 734–743 (2016)

    Article  Google Scholar 

  46. NOIZEUS database, http://ecs.utdallas.edu/loizou/speech/noizeus/

  47. M. Novey, T. Adali, Complex ICA by negentropy maximization. IEEE Trans. Neural Netw. Learn. Syst. 19(4), 596–609 (2008)

    Article  Google Scholar 

  48. D. Nuzillard, A. Bijaoui, Blind source separation and analysis of multispectral astronomical images. Astron. Astrophys. Suppl. Ser. 147(1), 129–138 (2000)

    Article  Google Scholar 

  49. E. Ollila, V. Koivunen, Complex ICA using generalized uncorrelating transform. Signal Process. 89(4), 365–377 (2009)

    Article  MATH  Google Scholar 

  50. L. Parra, C. Spence, Convolutive blind source separation of non-stationary sources. Proc. IEEE Trans. Speech Audio Process. 8(3), 320–327 (2000)

    Article  MATH  Google Scholar 

  51. G. Pendharkar, G.-R. Naik, H.T. Nguyen, Using blind source separation on accelerometry data to analyze and distinguish the toe walking gait from normal gait in ITW children. Biomed. Signal Process. Control 13, 41–49 (2014)

    Article  Google Scholar 

  52. D.-T. Pham, P. Garat, Blind separation of mixture of independent sources through aquasi-maximum likelihood approach. IEEE Trans. Signal Process. 45(7), 1712–1725 (1997)

    Article  MATH  Google Scholar 

  53. N. Rehman, D.-P. Mandic, Multivariate empirical mode decomposition. Proc. R. Soc. Lond. A. 466, 1291–1302 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  54. G. Rilling, P. Flandrin, P. Gonçalves, J.M. Lilly, Bivariate empirical mode decomposition. IEEE Signal Process. Lett. 14(12), 936–939 (2007)

    Article  Google Scholar 

  55. P. Smaragdis, Blind separation of convolved mixtures in the frequency domain. Neurocomputing 22(1–3), 21–34 (1998)

    Article  MATH  Google Scholar 

  56. J.-S. Smith, The local mean decomposition and its application to EEG perception data. J. R. Soc. Interface 2(5), 443–454 (2005)

    Article  Google Scholar 

  57. A.-K. Takahata, E.-Z. Nadalin, R. Ferrari, L.-T. Duarte, R. Suyama, R.-R. Lopes, J.M.-T. Romano, M. Tygel, Unsupervised processing of geophysical signals: a review of some key aspects of blind deconvolution and blind source separation. IEEE Signal Process. Mag. 29(4), 27–35 (2012)

    Article  Google Scholar 

  58. T. Tanaka, D.P. Mandic, Complex empirical mode decomposition. IEEE Signal Process. Lett. 14(2), 101–104 (2007)

    Article  Google Scholar 

  59. TIMIT database, https://catalog.ldc.upenn.edu/ldc93s1

  60. M.E. Torres, M.A. Colominas, G. Schlotthauer, P. Flandrin, A complete ensemble empirical mode decomposition with adaptive noise, in Proceeding of 36th ICASSP (2011)

  61. J. Traa, P. Smaragdis, Multichannel source separation and tracking with RANSAC and directional statistics. IEEE/ACM Trans. Audio Speech Lang. Process. 22(12), 2233–2243 (2014)

    Article  Google Scholar 

  62. Z. Wu, N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1–41 (2009)

    Article  Google Scholar 

  63. H.-C. Wu, J.-C. Principe, Simultaneous diagonalization in the frequency domain (SDIF) for source separation, in Proceeding of ICA (1999), pp. 245–250

  64. P. Xie, SL. Grant, A fast and efficient frequency-domain method for convolutive blind source separation, Proceeding of Region 5 Conference, IEEE (2008)

  65. M.-H. Yeh, The complex bidimensional empirical mode decomposition. Signal Process. 92(2), 523–541 (2012)

    Article  Google Scholar 

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  1. Laboratoire de Physique de Rayonnement et Applications, University of Jijel, BP 98, Ouled Aissa, Jijel, Algeria

    Mina Kemiha & Abdellah Kacha

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  1. Mina Kemiha
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  2. Abdellah Kacha
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Correspondence to Mina Kemiha.

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Kemiha, M., Kacha, A. Complex Blind Source Separation. Circuits Syst Signal Process 36, 4670–4687 (2017). https://doi.org/10.1007/s00034-017-0539-0

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