Abstract
In this paper we present a method for blind deconvolution of linear channels based on source separation techniques, for real word signals. This technique applied to blind deconvolution problems is based in exploiting not the spatial independence between signals but the temporal independence between samples of the signal. Our objective is to minimize the mutual information between samples of the output in order to retrieve the original signal. In order to make use of use this idea the input signal must be a non-Gaussian i.i.d. signal. Because most real world signals do not have this i.i.d. nature, we will need to preprocess the original signal before the transmission into the channel. Likewise we should assure that the transmitted signal has non-Gaussian statistics in order to achieve the correct function of the algorithm. The strategy used for this preprocessing will be presented in this paper. If the receiver has the inverse of the preprocess, the original signal can be reconstructed without the convolutive distortion.
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References
Amari, S., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. In: NIPS 1995, vol. 8. MIT Press, Cambridge (1996)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7 (1995)
Bellings, S.A., Fakhouri, S.Y.: Identification of a class of nonlinear systems using correlation analysis. Proc. IEEE 66, 691–697 (1978)
Boer, E.D.: Cross-correlation function of a bandpass nonlinear network. Proc. IEEE 64, 1443–1444 (1976)
Cardoso, J.P.: Source separation using higher order moments. In: Proc. ICASSP (1989)
Comon, P.: Independent component analysis, a new concept? Signal Processing 36 (1994)
Comon, P.: Separation of sources using higher order cumulants. Advanced Algorithms and Architectures for Signal Processing (1989)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunications (1991)
Jacoviti, G., Neri, A., Cusani, R.: Methods for estimating the autocorrelation function of complex stationary process. IEEE Trans. ASSP 35, 1126–1138 (1987)
Jutten, C., Hérault, J.: Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24 (1991)
Nguyen Thi, H.L., Jutten, C.: Blind source separation for convolutive mixtures. IEEE Transactions on Signal Processing 45(2) (1995)
Nikias, C.L., Petropulu, A.P.: Higher-Order Spectra Analysis – A Nonlinear Signal processing Framework. Prentice-Hall, Englewood Cliffs (1993)
Nikias, C.L., Raghuveer, M.R.: Bispectrum estimation: A digital signal processing framework. Proc. IEEE 75, 869–890 (1987)
Prakriya, S., Hatzinakos, D.: Blind identification of LTI-ZMNL-LTI nonlinear channel models. Biol. Cybern. 55, 135–144 (1985)
Puntonet, C., Mansour, A., Jutten, C.: A geometrical algorithm for blind separation of sources, GRETSI (1995)
Taleb, A., Jutten, C.: Nonlinear source separation: the post-nonlinear mixtures. In: Proc. ESANN (1997)
Taleb, A., Solé, J., Jutten, C.: Blind Inversion of Wiener Systems. In: Proc. IWANN (1999)
Taleb, A., Solé, J., Jutten, C.: Quasi-Nonparametric Blind Inversion of Wiener Systems. IEEE Transactions on Signal Processing 49(5), 917–924 (2001)
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© 2006 Springer-Verlag Berlin Heidelberg
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Solé-Casals, J., Monte-Moreno, E. (2006). Blind Channel Deconvolution of Real World Signals Using Source Separation Techniques. In: Faundez-Zanuy, M., Janer, L., Esposito, A., Satue-Villar, A., Roure, J., Espinosa-Duro, V. (eds) Nonlinear Analyses and Algorithms for Speech Processing. NOLISP 2005. Lecture Notes in Computer Science(), vol 3817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11613107_32
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DOI: https://doi.org/10.1007/11613107_32
Publisher Name: Springer, Berlin, Heidelberg
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