Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Adaptive Signal Separation with Maximum Likelihood

  • Zhao, Yongjian (School of Mechanical, Electrical & Information Engineering, Shandong University) ;
  • Jiang, Bin (School of Mechanical, Electrical & Information Engineering, Shandong University)
  • Received : 2018.06.26
  • Accepted : 2018.09.25
  • Published : 2020.02.29
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Abstract

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.

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References

  1. I. Goodfellow, Y. Bengio, and A. Courvile, Deep Learning. Cambridge, MA: The MIT Press, 2016.
  2. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York, NY: John Wiley & Sons, 2001.
  3. A. Cichocki, D. Mandic, L. De Lathauwer, G. Zhou, Q. Zhao, C. Caiafa, and H. A. Phan, "Tensor decompositions for signal processing applications: from two-way to multiway component analysis," IEEE Signal Processing Magazine, vol. 32, no. 2, pp. 145-163, 2015. https://doi.org/10.1109/MSP.2013.2297439
  4. R. Llinares, J. Igual, A. Salazar, and A. Camacho, "Semi-blind source extraction of atrial activity by combining statistical and spectral features," Digital Signal Processing, vol. 21, no. 2, pp. 391-403, 2011. https://doi.org/10.1016/j.dsp.2010.06.005
  5. C. D. Papadaniil and L. J. Hadjileontiadis, "Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features," IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 4, pp. 1138-1152, 2014. https://doi.org/10.1109/JBHI.2013.2294399
  6. H. Zhang, C. Wang, and X. Zhou, "An improved secure semi-fragile watermarking based on LBP and Arnold transform," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1382-1396, 2017. https://doi.org/10.3745/JIPS.02.0063
  7. C. Jutten and J. Herault, "Blind separation of sources. Part I: An adaptive algorithm based on neuromimetic architecture," Signal Processing, vol. 24, no. 1, pp. 1-10, 1991. https://doi.org/10.1016/0165-1684(91)90079-X
  8. J. Virta and K. Nordhausen, "Blind source separation of tensor-valued time series," Signal Processing, vol. 141, pp. 204-216, 2017. https://doi.org/10.1016/j.sigpro.2017.06.008
  9. C. Pehlevan, S. Mohan, and D. B. Chklovskii, "Blind nonnegative source separation using biological neural networks," Neural Computation, vol. 29, no. 11, pp. 2925-2954, 2017. https://doi.org/10.1162/neco_a_01007
  10. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: a review and new perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013. https://doi.org/10.1109/TPAMI.2013.50
  11. W. Y. Leong and D. P. Mandic, "Noisy component extraction (NoiCE)," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 57, no. 3, pp. 664-671, 2010. https://doi.org/10.1109/TCSI.2009.2024988
  12. G. Chabriel, M. Kleinsteuber, E. Moreau, H. Shen, P. Tichavsky, and A. Yeredor, "Joint matrices decompositions and blind source separation: a survey of methods, identification, and applications," IEEE Signal Processing Magazine, vol. 31, no. 3, pp. 34-43, 2014. https://doi.org/10.1109/MSP.2014.2298045
  13. J. Nikunen and T. Virtanen, "Direction of arrival based spatial covariance model for blind sound source separation," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 3, pp. 727-739, 2014. https://doi.org/10.1109/TASLP.2014.2303576
  14. Y. Zhao, B. Liu, and S. Wang, "A robust extraction algorithm for biomedical signals from noisy mixtures," Frontiers of Computer Science in China, vol. 5, no. 4, pp. 387-394, 2011. https://doi.org/10.1007/s11704-011-1043-5
  15. M. Taseska and E. A. Habets, "Blind source separation of moving sources using sparsity-based source detection and tracking," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 3, pp. 657-670, 2018. https://doi.org/10.1109/TASLP.2017.2780993
  16. E. Santana, J. C. Principe, E. E. Santana, R. C. S. Freire, and A. K. Barros, "Extraction of signals with specific temporal structure using kernel methods," IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5142-5150, 2010. https://doi.org/10.1109/TSP.2010.2053359
  17. S. Ferdowsi, S. Sanei, and V. Abolghasemi, "A predictive modeling approach to analyze data in EEG-fMRI experiments," International Journal of Neural Systems, vol. 25, no. 1, article no. 1440008, 2015.