Abstract
The blind separation of speech signals in reverberant environments is a well-known problem for which many algorithms have been developed. In this paper, we propose a novel initialization procedure for those ICA algorithms that work in the time-frequency domain and use the prewhitening of the observations. In comparison with classical initializations, this method allows to reduce drastically the number of permutations. The effectiveness of the proposed technique in realistic scenarios is illustrated by means of simulations.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hyvärinen, A.: Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. Neural Network 10(3), 626–634 (1999)
Cardoso, J.F.: Blind beamforming for non-Gaussian signals. IEE Proceedings-F, 362–370 (December 1993)
Pham, D.T., Cardoso, J.F.: Blind separation of instantaneous mixtures of non stationary sources. IEEE Trans. Signal Processing 49(9), 1837–1848 (2001)
Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 45(2), 434–444 (1997)
Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41(14), 1–24 (2001)
Ikram, M.Z., Morgan, D.R.: A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation. In: Proc. IEEE Int. Conf. on Acoustic, Speech and Signal Processing, pp. 881–884 (2002)
Sawada, H., Mukai, R., Araki, S., Makino, S.: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. In: Proc. Int. Conf. on Independent Component Analysis and Blind Source Separation, pp. 505–510 (2003)
Anemüller, J., Kollmeier, B.: Adaptive separation of acoustic sources for anechoic conditions: A constrained frequency domain approach. Speech Communication 39(1-2), 79–95 (2003)
Pham, D.T., Servire, C., Boumaraf, H.: Blind separation of convolutive audio mixtures using nonstationarity. In: Proc. of ICA 2003 Conference, Nara, Japan (April 2003)
Tichavsky, P., Koldovsky, Z.: Optimal paring of signal components separated by blind techniques. IEEE Signal Processing Letters 11(2), 119–122 (2004)
Cruces, S., Cichocki, A., De Lathauwer, L.: Thin QR and SVD factorizations for simultaneous Blind Signal Extraction. In: Proc. of the European Signal Processing Conference (EUSIPCO), Viena, Austria, pp. 217–220 (2004)
Fèvotte, C., Gribonval, R., Vincent, E.: BSS_EVAL toolbox user guide, IRISA, Rennes, France, Tech. Rep. 1706, (2005), http://www.irisa.fr/metiss/bss_eval/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sarmiento, A., Cruces, S., Durán, I. (2009). Improvement of the Initialization of ICA Time-Frequency Algorithms for Speech Separation. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-00599-2_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00598-5
Online ISBN: 978-3-642-00599-2
eBook Packages: Computer ScienceComputer Science (R0)