Abstract
We propose a new algorithm for solving the Blind Signal Separation (BSS) problem for convolutive mixing completely in the time domain. The closed form expressions used for first and second order optimization techniques derived in [1] for the instantaneous BSS case are extended to accommodate the more practical convolutive mixing scenario. Traditionally convolutive BSS problems are solved in the frequency domain [2–4] but this requires additional solving of the inherent frequency permutation problem. Where this is good for higher order systems, systems with a low to medium number of variables benefit from not being subject to a transform such as the DFT. We demonstrate the performance of the algorithm using two optimization methods with a convolutive synthetic mixing system and real speech data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
M. Joho and K. Rahbar, “Joint diagonalization of correlation matrices by using Newton methods with application to blind signal separation,” in Proc. Sensor Array and Multichannel Signal Processing Workshop (SAM), Rosslyn, VA, USA, Aug. 2002, pp. 403–407.
K. Rahbar and J. Reilly, “A New Frequency Domain Method for Blind Source Separation of Convolutive Audio Mixtures,” Submitted to IEEE Trans. on Speech and Audio Processing, January 2003.
S. Ikeda and N. Murata, “A method of ICA in Time-Frequency Domain,” in Proc. ICA, Aussois, January 1999, pp. 365–361.
N. Murata, “An Approach to Blind Source Separation of Speech Signals,” Proceedings of the 8th International Conference on Artificial Neural Networks, vol. 2, pp. 761–766, September 1998.
K. J. Pope and R. E. Bogner, “Blind signal separation I: Linear, instantaneous combinations,” Digital Signal Processing, vol. 6, no. 1, pp. 5–16, Jan. 1996.
K. J. Pope and R. E. Bogner, “Blind signal separation II: Linear, convolutive combinations,” Digital Signal Processing, vol. 6, no. 1, pp. 17–28, Jan. 1996.
M. Feng and K.-D. Kammeyer, “Blind source separation for communication signals using antenna arrays,” in Proc. ICUPC-98, Florence, Italy, Oct. 1998.
T. Petermann, D. Boss, and K. D. Kammeyer, “Blind GSM Channel Estimation Under Channel Coding Conditions,” Phoenix, USA, December 1999, pp. 180–185.
J. Larsen, L. Hansen, T. Kolenda, and F. Nielsen, “Independent Component Analysis in Multimedia Modelling,” in Proc. ICA, Nara, Japan, April 2003, pp. 687–696.
M. Z. Ikram and D. R. Morgan, “Exploring permutation inconsistency in Blind Separation of Speech Signals in a Reverberant Environment,” in Proc. ICASSP, Instanbul, Turkey, June 2000, pp. 1041–1044.
S. Amari, S. Douglas, A. Cichocki, and H. Yang, “Multichannel blind deconvolution and equalization using the natural gradient,” Paris, France, April 1997, pp. 101–104, Proceedings First IEEE Workshop on Signal Processing Advances in Wireless Communications.
Kenneth Holmström, “User’s Guide for TOMLAB v4.0,” URL: http://tomlab.biz/docs/tomlabv4.pdf, Sept. 2 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science + Business Media, Inc.
About this chapter
Cite this chapter
Russell, I.T., Xi, J., Mertins, A. (2005). Time Domain Blind Separation of Nonstationary Convolutively Mixed Signals. In: Wysocki, T.A., Honary, B., Wysocki, B.J. (eds) Signal Processing for Telecommunications and Multimedia. Multimedia Systems and Applications Series, vol 27. Springer, Boston, MA. https://doi.org/10.1007/0-387-22928-0_2
Download citation
DOI: https://doi.org/10.1007/0-387-22928-0_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-22847-1
Online ISBN: 978-0-387-22928-7
eBook Packages: EngineeringEngineering (R0)