Undecimated Wavelet Packet for Blind Speech Separation Using Independent Component Analysis

1. Comon, P.: Independent component analysis: A new concept? Signal Processing 36(3), 287–314 (1994)

   Article  MATH  Google Scholar 

2. Bell, A.J., Sejnowski, T.J.: An information maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1004–1034 (1995)

   Article  Google Scholar 

3. Cardoso, J.F.: Infomax and maximum likelihood for blind separation. IEEE Signal Processing Letters 4, 112–114 (1997)

   Article  Google Scholar 

4. Wang, F.S., Li, H.W., Li, R.: Novel NonGaussianity Measure Based BSS Algorithm for Dependent Signals. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb/WAIM 2007. LNCS, vol. 4505, pp. 837–844. Springer, Heidelberg (2007)

   Chapter  Google Scholar 

5. Xiao, W., Jingjing, H., Shijiu, J., Antao, X., Weikui, W.: Blind separation of speech signals based on wavelet transform and independent component analysis. Transactions of Tianjin University 16(2), 123–128 (2010)

   Article  Google Scholar 

6. Hyvärine, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)

   Article  Google Scholar 

7. Fowler, J.: The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters 12(9), 629–632 (2005)

   Article  Google Scholar 

8. Shensa, M.: The discrete wavelet transform: Wedding the à trous and Mallat algorithms. IEEE Trans. Signal Processing 40(10), 2464–2482 (1992)

   Article  MATH  Google Scholar 

9. Tasmaz, H., Ercelebi, E.: Speech enhancement based on undecimated wavelet packet-perceptual filterbanks and MMSE-STSA estimation in various noise environments. Digital Signal Processing 18(5), 797–812 (2008)

   Article  Google Scholar 

10. Cardoso, J.F.: Higher-order contrasts for independent component analysis. Neural Computation 11, 157–192 (1999)

    Article  Google Scholar 

11. Vincent, E., Gribonval, R., Fevotte, C.: Performance Measurement in Blind Audio Source Separation. IEEE Transactions on Audio, Speech, and Language Processing 14(4), 1462–1469 (2006)

    Article  Google Scholar 

12. Chien, J.T., Chen, B.C.: A New Independent Component Analysis for Speech Recognition and Separation. IEEE Transactions on Audio, Speech and Language Processing 14(4), 1245–1254 (2006)

    Google Scholar 

13. Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A blind source separation technique using second order statistics. IEEE Trans. Signal Processing 45, 434–444 (1997)

    Article  Google Scholar 

14. Gargour, C., Abrea, M., Ramachandran, V., Lina, J.M.: A short introduction to wavelets and their applications. IEEE Circuits and Systems Magazine 9(2), 57–58 (2009)

    Article  Google Scholar 

15. Coifman, R., Wickerhausser, M.: Entropy-based algorithms for best-basis selection. IEEE Transactions on Information Theory 38, 713–718 (1992)

    Article  MATH  Google Scholar 

16. ITU-T P.862, Perceptual evaluation of speech quality (PESQ), an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs. International Telecommunication Union, Geneva (2001)

    Google Scholar 

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

    Book  Google Scholar 

18. Wang, L., Brown, G.J.: Computational Auditory Scene Analysis: Principles, Algorithms, and Applications. Wiley/IEEE Press, Hoboken, NJ (2006)

    Book  Google Scholar 

19. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice-Hall, Englewood Cliffs (2008)

    Google Scholar 

20. Cichocki, A., Amari, S.: Adaptive Blind Signal and Adaptive Blind Signal and Image Processing. John Wiley and Sons, New York (2002)

    Book  Google Scholar 

21. Mallat: A Wavelet Tour of Signal Processing: The Sparse Way, 3rd edn. Academic Press, London (2008)

    MATH  Google Scholar 

22. Moussaoui, R., Rouat, J., Lefebvre, R.: Wavelet Based Independent Component Analysis for Multi-Channel Source Separation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 645–648 (2006)

    Google Scholar 

23. Usman, K., Juzoji, H., Nakajima, I., Sadiq, M.A.: A study of increasing the speed of the independent component analysis (lCA) using wavelet technique. In: Proc. International Workshop on Enterprise Networking and Computing in Healthcare Industry, pp. 73–75 (2004)

    Google Scholar 

24. Tanaka, T., Cichocki, A.: Subband decomposition independent component analysis and new performance criteria. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 541–544 (2004)

    Google Scholar 

25. Mirarab, M.R., Sobhani, M.A., Nasiri, A.A.: A New Wavelet Based Blind Audio Source Separation Using Kurtosis. In: International Conference on Advanced Computer Theory and Engineering (2010)

    Google Scholar 

26. Walden, A.T., Contreras, C.: The phase-corrected undecimated discrete wavelet packet transform and its application to interpreting the timing of events. Proceedings of the Royal Society of London, 2243–2266 (1998)

    Google Scholar 

27. Chien, J.T., Hsieh, H.L., Furui, S.: A new mutual information measure for independent component alalysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1817–1820 (2008)

    Google Scholar 

28. Fisher, W., Dodington, G., Goudie-Marshall, K.: The TIMIT-DARPA speech recognition research database: Specification and status. In: DARPA Workshop on Speech Recognition (1986)

    Google Scholar 

29. Zhang, W., Rao, B.D.: Combining Independent Component Analysis with Geometric Information and its Application to Speech Processing. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (2009)

    Google Scholar 

30. Fevotte, C., Gribonval, R., Vincent, E.: BSS EVAL toolbox user guide, IRISA, Rennes, France, Technical Report 1706 (2005)

    Google Scholar 

Download references