Blind Source Separation of Speech Signals Using a One-Dimensional Block DUET Algorithm
Subject Areas : electrical and computer engineeringS. S. Fadaei 1 * , M. H. Kahaei 2
1 - University of Science and Technology
2 -
Keywords: Time-frequencyDUEThistogramBSS,
Abstract :
To separate speech signals using blind techniques, the DUET algorithm is used in which each source signal is separated by masking the mixed signals in the Time-Frequency domain. To do so, a two dimensional Histogram of mixed parameters is generated which is computationally burden, and thus, can not be used in real-time. In this paper, we introduce a new algorithm in which the separation process can be carried out online. Also, simulation results show that this algorithm has a comparable precision with respect to the DUET algorithm.
[1] M. Baeck and U. Zolzer, "Real - time implementation of a source separation algorithm," in Proc. of the 6th Int. Conf. on Digital Audio Effects (DAFx’03), vol. 2, pp. 233-238, London, Sep. 2003.
[2] C. Jutten and J. Herault, "Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture," in Signal Processing, vol. 24, pp. 1-10, Jul. 1991.
[3] A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis, Wiley, New-York, 2001.
[4] J. Eriksson and V. Koivunen, "Identifiability, separability, and uniqueness of linear ICA models," in IEEE Signal Processing Letters, vol. 11, pp. 601-604, Jul. 2004.
[5] Z. M. Kamran, A. Rahim Leyman, and K. Abed–Meraim,"Techniques for blind source separation using higher-order statistics," in Proc. of the Tenth IEEE Workshop Statistical Signal and Array Processingpp. 334-338, Aug. 2000.
[6] A. Belouchrani, A. Meraim, J. F. Cardoso, and E. Moulines, "Ablind source separation technique using second - order statistics," IEEE Trans. Signal Processing, vol. 45, no. 2, pp. 434-444,Feb. 1997.
[7] D. T. Pham and J. F. Cardoso, "Blind separation of instantaneous mixtures of nonstationary sources," IEEE Trans. Signal Processing, vol. 49, no. 9, pp. 1837-1848, Sep. 2001.
[8] A. Belouchrani and M. G. Amin, "Blind source separation based on time - frequency signal representations," IEEE Trans. Signal Processing, vol. 46, no. 11, pp. 2888-2897, Nov. 1998.
[9] J. Ye, B. Xu, and F. Liu, "An improved cross - correlation based method of blind source separation in frequency domain," in Proc. IEEE Int. Conf. Neural Networks & Signal Processing, vol. 3,pp. 1370-1374, Dec. 2003.
[10] O. Yilmaz and S. Rickard, "Blind separation of speech mixtures via time-frequency masking," IEEE Trans. on Signal Processing, vol. 52, no. 7, pp. 1830-1847, Jul. 2004.
[11] A. Jourjine, S. Rickard, and O. Yilmaz, "Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures," in Proc. IEEE Int. Conf. Acoustic, Speech, Signal Processing, vol. 5,pp. 2985-2988, Istanbul, Turkey, Jun. 2000.
[12] S. Rickard and O. Yilmaz, "On the approximate W – disjoint orthogonality of speech," in Proc. IEEE Int. Conf. Acoustic, Speech, Signal Processing, vol. 1, pp. 529-532, Orlando, US, May 2002.
[13] S. Rickard, R. Balan, and J. Rosca, "Real-time time-frequency based blind source separation," in Proc. Int. Workshop Independent Component Analysis Blind Source Separation, vol. 5, pp. 651-656,San Diego, US, Dec. 2001.
[14] http://alum.mit.edu/www/rickard/bss.html