Fast and accurate methods of independent component analysis: A survey

Petr Tichavský; Zbyněk Koldovský

Kybernetika (2011)

  • Volume: 47, Issue: 3, page 426-438
  • ISSN: 0023-5954

Abstract

top
This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG).

How to cite

top

Tichavský, Petr, and Koldovský, Zbyněk. "Fast and accurate methods of independent component analysis: A survey." Kybernetika 47.3 (2011): 426-438. <http://eudml.org/doc/196876>.

@article{Tichavský2011,
abstract = {This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG).},
author = {Tichavský, Petr, Koldovský, Zbyněk},
journal = {Kybernetika},
keywords = {Blind source separation; probability distribution; score function; autoregressive random processes; audio signal processing; electroencephalogram; artifact rejection},
language = {eng},
number = {3},
pages = {426-438},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Fast and accurate methods of independent component analysis: A survey},
url = {http://eudml.org/doc/196876},
volume = {47},
year = {2011},
}

TY - JOUR
AU - Tichavský, Petr
AU - Koldovský, Zbyněk
TI - Fast and accurate methods of independent component analysis: A survey
JO - Kybernetika
PY - 2011
PB - Institute of Information Theory and Automation AS CR
VL - 47
IS - 3
SP - 426
EP - 438
AB - This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG).
LA - eng
KW - Blind source separation; probability distribution; score function; autoregressive random processes; audio signal processing; electroencephalogram; artifact rejection
UR - http://eudml.org/doc/196876
ER -

References

top
  1. Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., Moulines, E., 10.1109/78.554307, IEEE Trans. Signal Processing 45 (1997), 434–444. (1997) DOI10.1109/78.554307
  2. Boscolo, R., Pan, H., Roychowdhury, V. P., 10.1109/TNN.2003.820667, IEEE Trans. Neural Networks 15 (2004), 55–65. (2004) DOI10.1109/TNN.2003.820667
  3. Cardoso, J.-F., Blind signal separation: statistical principles, Proc. IEEE 90 (1998), 2009–2026. (1998) 
  4. Cardoso, J.-F., Pham, D. T., Separation of non stationary sources, Algorithms and performance., In: Independent Components Analysis: Principles and Practice (S. J. Roberts and R. M. Everson, eds.), Cambridge University Press 2001, pp. 158–180. (2001) 
  5. Chambers, J., Cleveland, W., Kleiner, B., Tukey, P., Graphical Methods for Data Analysis, Wadsworth, 1983. (1983) Zbl0532.65094
  6. Cruces, S., Cichocki, A., Lathauwer, L. De, Thin QR and SVD factorizations for simultaneous blind signal extraction, In: Proc. European Signal Processing Conference (EUSIPCO), Vienna 2004, pp. 217–220. (2004) 
  7. Dégerine, S., Zaïdi, A., 10.1109/TSP.2004.827195, IEEE Trans. Signal Processing 52 (2004), 1492–1512. (2004) MR2068987DOI10.1109/TSP.2004.827195
  8. Delorme, A., Sejnowski, T., Makeig, S., 10.1016/j.neuroimage.2006.11.004, Neuroimage 34 (2007) 1443–1449. (2007) DOI10.1016/j.neuroimage.2006.11.004
  9. Hyvärinen, A., Oja, E., 10.1162/neco.1997.9.7.1483, Neural Computation 9 (1997), 1483–1492. (1997) DOI10.1162/neco.1997.9.7.1483
  10. Hyvärinen, A., Karhunen, J., Oja, E., Independent Component Analysis, John Wiley & Sons, 2001. (2001) 
  11. James, C. J., Hesse, C. W., 10.1088/0967-3334/26/1/R02, Physiol. Meas. 26 (2005), R15–R39. (2005) DOI10.1088/0967-3334/26/1/R02
  12. Koldovský, Z., Tichavský, P., Oja, E., 10.1109/TNN.2006.875991, IEEE Trans. Neural Networks 17 (2006), 1265–1277. (2006) DOI10.1109/TNN.2006.875991
  13. Koldovský, Z., Málek, J., Tichavský, P., Deville, Y., Hosseini, S., Blind separation of piecewise stationary nonGaussian Sources, Signal Process. 89 (2009), 2570–2584. (2009) 
  14. Koldovský, Z., Tichavský, P., A comparison of independent component and independent subspace analysis algorithms, In: Proc. European Signal Processing Conference (EUSIPCO), Glasgow 2009, pp. 1447–1451. (2009) 
  15. Koldovský, Z., Tichavský, P., 10.1109/TASL.2010.2049411, IEEE Trans. Audio, Speech and Language Processing 19 (2011), 406–416. (2011) DOI10.1109/TASL.2010.2049411
  16. Learned-Miller, E. G., III, J. W. Fisher, ICA using spacings estimates of entropy, J. Machine Learning Research 4 (2004), 1271–1295. (2004) MR2103630
  17. Lee, Te-Won, Independent Component Analysis, Theory and Applications, Kluwer Academic Publishers, 1998. (1998) Zbl0910.94004
  18. Pham, D. T., Garat, P., 10.1109/78.599941, Signal Process. 45 (1997), 1712–1725. (1997) DOI10.1109/78.599941
  19. Pham, D.-T., 10.1137/S089547980035689X, SIAM J. Matrix Anal. Appl. 22 (2001), 1136–1152. (2001) Zbl1008.65020MR1824062DOI10.1137/S089547980035689X
  20. Pham, D.-T., 10.1016/S0165-1684(00)00260-7, Signal Process. 81 (2001), 855–870. (2001) Zbl1098.94576DOI10.1016/S0165-1684(00)00260-7
  21. Pham, D.-T., Cardoso, J.-F., 10.1109/78.942614, IEEE Trans. Signal Process. 49 (2001), 1837–1848. (2001) MR1852133DOI10.1109/78.942614
  22. Tichavský, P., Koldovský, Z., Oja, E., 10.1109/TSP.2006.870561, IEEE Trans. Signal Process. 54 (2006), 1189–1203. (2006) DOI10.1109/TSP.2006.870561
  23. Tichavský, P., Koldovský, Z., Oja, E., 10.1109/TSP.2007.910503, IEEE Tran. Signal Process. 56 (2008), 1715–1716. (2008) MR2516583DOI10.1109/TSP.2007.910503
  24. Tichavský, P., Koldovský, Z., Oja, E., Speed and accuracy enhancement of linear ICA techniques using rational nonlinear functions, Lecture Notes in Comput. Sci. 4666 (2007), 285–292. (2007) Zbl1172.94493
  25. Tichavský, P., Koldovský, Z., Yeredor, A., Gomez-Herrero, G., Doron, E., 10.1109/TNN.2007.908648, IEEE Trans. Neural Networks 19 (2008), 421–430. (2008) DOI10.1109/TNN.2007.908648
  26. Tichavský, P., Yeredor, A., 10.1109/TSP.2008.2009271, IEEE Trans. Signal Process. 57 (2009), 878–891. (2009) MR3027773DOI10.1109/TSP.2008.2009271

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.