Cover
Vol. 11 No. 1 (2015)

Published: July 31, 2015

Pages: 1-9

Original Article

Independent Component Analysis for Separation of Speech Mixtures: A Comparison Among Thirty Algorithms

Ali Al-Saegh 1
1Computer Engineering Department University of Mosul Mosul, Iraq
History
  • Received: May 31, 2015
  • Available Online: July 31, 2015
DOI

https://doi.org/10.37917/ijeee.11.1.1

Abstract

Vast number of researches deliberated the separation of speech mixtures due to the importance of this field of research . Whereas its applications became widely used in our daily life; such as mobile conversation, video conferences, and other distant communications. These sorts of applications may suffer from what is well known the cocktail party problem. Independent component analysis (ICA) has been extensively used to overcome this problem and many ICA algorithms based on different techniques have been developed in this context. Still coming up with some suitable algorithms to separate speech mixed signals into their original ones is of great importance. Hence, this paper utilizes thirty ICA algorithms for estimating the original speech signals from mixed ones, the estimation process is carried out with the purpose of testing the robustness of the algorithms once against a different number of mixed signals and another against different lengths of mixed signals. Three criteria namely Spearman correlation coefficient, signal to interference ratio, and computational demand have been used for comparing the obtained results. The results of the comparison were sufficient to signify some algorithms which are appropriate for the separation of speech mixtures.

Keywords

References

  1. A. Hyvärinen, J. Karhunen, and E. Oja, independent component analysis . John Wiley and Sons, 2001, p. 481.
  2. M. Castella and J.-C. Pesquet, “An Iterative Blind Source Separation Method for Convolutive Mixtures of Blind Signal Separation , vol. 3195, C. Puntonet and A. Prieto, Eds. Springer Berlin / Heidelberg, 2004, pp. 922–929.
  3. S. Douglas and M. Gupta, “Spatio-Temporal FastICA algorithms for the blind separation of convolutive mixtures,” IEEE Trans. Audio. Speech. Lang. Processing , vol. 15, no. 5, pp. 1511–1520, 2007.
  4. M. S. Pedersen, D. Wang, J. Larsen, and U. Kjems, “Two-microphone separation of speech mixtures.,” Mar. 2008.
  5. J. Zhang and P. Ching, “Short-Time ICA for Blind Separation of Noisy Speech,” in ISCSLP 2006 , 2006, no. 1.
  6. R. Prasad, H. Saruwatari, and K. Shikano, “An ICA algorithm for separation of convolutive mixture of speech signals,” Int. J. Signal … , pp. 551–561, 2004.
  7. J. Chien and B. Chen, “A new independent component analysis for speech recognition and separation,” Audio, Speech, Lang. Process. … , vol. 14, no. 4, pp. 1245– 1254, 2006.
  8. K. Mohanaprasad and P. Arulmozhivarman, “Comparison of Fast ICA and Gradient Algorithms of Speech Signals,” Int. J. Eng. Technol. , vol. 5, no. 4, pp. 3196–3202, 2013.
  9. A. D. Back, A. S. Weigend, L. N. Stern, and S. Is--, “A First Application of Independent Component Analysis to Extracting Structure from Stock Returns A First Application of Independent Component Analysis t o Extracting Structure from Stock Returns,” Int. J. , no. November, pp. 1–8, 1997.
  10. M. Novey and T. Adali, “Adaptable Nonlinearity for Complex Maximization of Nongaussianity and a FixedPoint Algorithm,” 2006 16th IEEE Signal Process. Soc. Work. Mach. Learn. Signal Process. , no. x, pp. 79–84, Sep. 2006.
  11. P. Tichavský, Z. Koldovský, and E. Doron, “Blind signal separation by combining two ICA algorithms: HOS-based EFICA and time structure-based WASOBI,” Eur. Signal Process. … , pp. 4–8, 2006.
  12. D. N. Rutledge and D. Jouan-Rimbaud Bouveresse, “Independent Components Analysis with the JADE algorithm,” TrAC Trends Anal. Chem. , 2013.
  13. A. Bell and T. J. Sejnowski, “An InformationMaximization Approach to Blind Separation and Blind Deconvolution,” Neural Comput. , vol. 7, pp. 1129– 1159, 1995.
  14. A. Belouchrani, K. Abed-Meraim, J. F. Cardoso, and E. Moulines, “Second-order blind separation of temporally correlated sources,” in Proc. Int. Conf. on Digital Sig. Proc.,(Cyprus) , 1993, pp. 346–351.
  15. E. G. Learned-Miller and J. W. Fisher III, “Independent components analysis by direct entropy minimization,” UC Berkeley Tech. Rep. , 2003.
  16. J. J. Murillo-Fuentes, R. Boloix-Tortosa, and F. J. 4th Int. Symp. Indep. Comp. Anal. Blind Signal Separat , 2003, pp. 1–4.
  17. L. Tong, V. C. Soon, Y. F. Huang, and R. Liu, “AMUSE: a new blind identification algorithm,” in Circuits and Systems, 1990., IEEE International Symposium on , 1990, pp. 1784–1787.
  18. M. Novey and T. Adali, “On Extending the Complex FastICA Algorithm to Noncircular Sources,” IEEE Trans. Signal Process. , vol. 56, no. 5, pp. 2148–2154, May 2008.
  19. A. Reda, S. Panjwani, and E. Cutrell, “Hyke: a low-cost remote attendance tracking system for developing regions,” in NSDR’11 , 2011.
  20. M. Klemm, J. Haueisen, and G. Ivanova, “Independent component analysis: comparison of algorithms for the Biol. Eng. Comput. , vol. 47, no. 4, pp. 413–23, Apr. 2009.