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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 439264, 13 pages
http://dx.doi.org/10.1155/2015/439264
Research Article

A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics

1Department of Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
2College of Urban Railway Transportation, Shanghai University of Engineering and Science, Shanghai, China

Received 1 April 2015; Accepted 22 July 2015

Academic Editor: Carla Roque

Copyright © 2015 Guangkuo Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Takahashi, Y. Anzai, and Y. Sakurai, “A new approach to spike sorting for multi-neuronal activities recorded with a tetrode—how ICA can be practical,” Neuroscience Research, vol. 46, no. 3, pp. 265–272, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Tonazzini, E. Salerno, and L. Bedini, “Fast correction of bleed-through distortion in grayscale documents by a blind source separation technique,” International Journal on Document Analysis and Recognition, vol. 10, no. 1, pp. 17–25, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Ristaniemi and J. Joutsensalo, “Learning algorithms for blind multiuser detection in CDMA downlink,” in Proceedings of the 9th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 3, pp. 1040–1044, September 1998. View at Scopus
  4. L. H. Sibul, M. J. Roan, and J. Erling, “Deconvolution and signal extraction in geophysics and acoustics,” The Journal of the Acoustical Society of America, vol. 112, no. 5, p. 2389, 2002. View at Publisher · View at Google Scholar
  5. L. T. Duarte, R. Lopes, J. H. Faccipieri et al., “Separation of reflection from diffraction events via the CRS technique and a blind source separation method based on sparsity maximization,” in Proceedings of the 13th International Congress of the Brazilian Geophysical Society, 2013.
  6. B. Rivet, L. Girin, and C. Jutten, “Mixing audiovisual speech processing and blind source separation for the extraction of speech signals from convolutive mixtures,” IEEE Transactions on Audio, Speech and Language Processing, vol. 15, no. 1–4, pp. 96–108, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. H.-M. Park, H.-Y. Jung, T.-W. Lee, and S.-Y. Lee, “Subband-based blind signal separation for noisy speech recognition,” Electronics Letters, vol. 35, no. 23, pp. 2011–2012, 1999. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Ciaramella, E. De Lauro, S. De Martino, B. Di Lieto, M. Falanga, and R. Tagliaferri, “Characterization of Strombolian events by using independent component analysis,” Nonlinear Processes in Geophysics, vol. 11, no. 4, pp. 453–461, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. E. De Lauro, S. De Martino, M. Falanga, and M. Palo, “Decomposition of high-frequency seismic wavefield of the Strombolian-like explosions at Erebus volcano by independent component analysis,” Geophysical Journal International, vol. 177, no. 3, pp. 1399–1406, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. E. de Lauro, S. de Martino, E. Esposito, M. Falanga, and E. P. Tomasini, “Analogical model for mechanical vibrations in flue organ pipes inferred by independent component analysis,” The Journal of the Acoustical Society of America, vol. 122, no. 4, pp. 2413–2424, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Capuano, E. De Lauro, S. De Martino, and M. Falanga, “Analysis of water level oscillations by using methods of nonlinear dynamics,” International Journal of Modern Physics B, vol. 23, no. 28-29, pp. 5530–5542, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Capuano, E. De Lauro, S. De Martino, and M. Falanga, “Water-level oscillations in the Adriatic Sea as coherent self-oscillations inferred by independent component analysis,” Progress in Oceanography, vol. 91, no. 4, pp. 447–460, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. P. Comon, “Independent component analysis, a new concept?” Signal Processing, vol. 36, no. 3, pp. 287–314, 1994. View at Publisher · View at Google Scholar · View at Scopus
  14. E. S. Warner and I. K. Proudler, “Single-channel blind signal separation of filtered MPSK signals,” IEE Proceedings: Radar, Sonar and Navigation, vol. 150, no. 6, pp. 396–402, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Servière and P. Fabry, “Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic,” Mechanical Systems and Signal Processing, vol. 19, no. 6, pp. 1293–1311, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Barry, D. Fitzgerald, E. Coyle, and B. Lawlor, “Drum source separation using percussive feature detection and spectral modulation,” in Proceedings of the IEE Irish Signals and Systems Conference, pp. 13–17, September 2005. View at Scopus
  17. J. Taelman, S. Van Huffel, and A. Spaepen, “Wavelet-independent component analysis to remove electrocardiography contamination in surface electromyography,” IEEE Engineering in Medicine and Biology Magazine, vol. 1, pp. 682–685, 2007. View at Google Scholar
  18. B. Mijović, M. de Vos, I. Gligorijević, J. Taelman, and S. van Huffel, “Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 9, pp. 2188–2196, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Guo, S. Huang, and Y. Li, “Single-mixture source separation using dimensionality reduction of ensemble empirical mode decomposition and independent component analysis,” Circuits, Systems, and Signal Processing, vol. 31, no. 6, pp. 2047–2060, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. H. D. I. Abarbanel, T. W. Frison, and L. S. Tsimring, “Obtaining order in a world of chaos,” IEEE Signal Processing Magazine, vol. 15, no. 3, pp. 49–65, 1998. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Haykin and J. Principe, “Making sense of a complex world,” IEEE Signal Processing Magazine, vol. 15, no. 3, pp. 66–81, 1998. View at Publisher · View at Google Scholar · View at Scopus
  22. F. Takens, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick 1980, pp. 366–381, 1981. View at Google Scholar
  23. C. J. James and D. Lowe, “Single channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework,” in Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 1974–1977, October 2001. View at Scopus
  24. M. E. Davies and C. J. James, “Source separation using single channel ICA,” Signal Processing, vol. 87, no. 8, pp. 1819–1832, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. H.-G. Ma, Q.-B. Jiang, Z.-Q. Liu, G. Liu, and Z.-Y. Ma, “A novel blind source separation method for single-channel signal,” Signal Processing, vol. 90, no. 12, pp. 3232–3241, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  26. P. Bernaola-Galván, P. C. Ivanov, L. A. N. Nunes Amaral, and H. E. Stanley, “Scale invariance in the nonstationarity of human heart rate,” Physical Review Letters, vol. 87, no. 16, pp. 168–170, 2001. View at Google Scholar · View at Scopus
  27. G. Tzagkarakis, M. Papadopouli, and P. Tsakalides, “Singular spectrum analysis of traffic workload in a large-scale wireless LAN,” in Proceedings of the 10th ACM Symposium on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM '07), pp. 99–108, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Casdagli, S. Eubank, J. D. Farmer, and J. Gibson, “State space reconstruction in the presence of noise,” Physica D: Nonlinear Phenomena, vol. 51, no. 1–3, pp. 52–98, 1991. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. M. B. Kennel, R. Brown, and H. D. I. Abarbanel, “Determining embedding dimension for phase-space reconstruction using a geometrical construction,” Physical Review A, vol. 45, no. 6, pp. 3403–3411, 1992. View at Publisher · View at Google Scholar · View at Scopus
  30. D. S. Broomhead and G. P. King, “Extracting qualitative dynamics from experimental data,” Physica D: Nonlinear Phenomena, vol. 20, no. 2-3, pp. 217–236, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  31. D. Kugiumtzis, “State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length,” Physica D: Nonlinear Phenomena, vol. 95, no. 1, pp. 13–28, 1996. View at Publisher · View at Google Scholar · View at Scopus
  32. A. M. Fraser and H. L. Swinney, “Independent coordinates for strange attractors from mutual information,” Physical Review A, vol. 33, no. 2, pp. 1134–1140, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  33. A. Swami and J. M. Mendel, “Cumulant-based approach to harmonic retrieval and related problems,” IEEE Transactions on Signal Processing, vol. 39, no. 5, pp. 1099–1109, 1991. View at Publisher · View at Google Scholar · View at Scopus
  34. J. M. M. Anderson, G. B. Giannakis, and A. Swami, “Harmonic retrieval using higher order statistics: a deterministic formulation,” IEEE Transactions on Signal Processing, vol. 43, no. 8, pp. 1880–1889, 1995. View at Publisher · View at Google Scholar · View at Scopus
  35. R. J. Povinelli, M. T. Johnson, A. C. Lindgren, F. M. Roberts, and J. Ye, “Statistical models of reconstructed phase spaces for signal classification,” IEEE Transactions on Signal Processing, vol. 54, no. 6 I, pp. 2178–2186, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. Z. Xie and K. Wang, “Selection of embedding parameters in phase space reconstruction,” in Proceedings of the IEEE 2nd International Conference on Intelligent Computing Technology and Automation (ICICTA '09), vol. 4, pp. 637–640, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. A. M. Fraser, “Reconstructing attractors from scalar time series: a comparison of singular system and redundancy criteria,” Physica D: Nonlinear Phenomena, vol. 34, no. 3, pp. 391–404, 1989. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  38. J. M. Mendel, “Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications,” Proceedings of the IEEE, vol. 79, no. 3, pp. 278–305, 1991. View at Publisher · View at Google Scholar · View at Scopus
  39. W. A. Porter and W. Liu, “Steering high order moment calculations from lower-dimensional spaces,” Information Sciences, vol. 80, no. 3-4, pp. 181–194, 1994. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  40. M. Casdagli, S. Eubank, J. D. Farmer, and J. Gibson, “State space reconstruction in the presence of noise,” Physica D. Nonlinear Phenomena, vol. 51, no. 1–3, pp. 52–98, 1991. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  41. K. Kobayashi, C. J. James, T. Nakahori, T. Akiyama, and J. Gotman, “Isolation of epileptiform discharges from unaveraged EEG by independent component analysis,” Clinical Neurophysiology, vol. 110, no. 10, pp. 1755–1763, 1999. View at Publisher · View at Google Scholar · View at Scopus
  42. D. T. Pham, “Mutual information approach to blind separation of stationary sources,” IEEE Transactions on Information Theory, vol. 48, no. 7, pp. 1935–1946, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  43. R. Aichner, H. Buchner, F. Yan, and W. Kellermann, “A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments,” Signal Processing, vol. 86, no. 6, pp. 1260–1277, 2006. View at Publisher · View at Google Scholar · View at Scopus