Abstract
This paper introduces a Recurrent Flexible ICA approach to a novel blind sources separation problem in convolutive nonlinear environment. The proposed algorithm performs the separation after the convolutive mixing of post nonlinear convolutive mixtures. The recurrent neural network produces the separation by minimizing the output mutual information. Experimental results are described to show the effectiveness of the proposed technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jutten, C., Karhunen, J.: Advances in Nonlinear Blind Sources Separation. In: 4th International Symposium on ICA and BSS (ICA 2003), Nara, Japan (April 2003)
Taleb, A.: A Generic Framework for Blind Sources Separation in Structured Nonlinear Models. IEEE Trans. on signal processing 50(8) (2002)
Taleb, A., Jutten, C.: Sources Separation in post nonlinear mixtures. IEEE Trans. on signal processing 47(10) (1999)
Hyvarinen, A., Pajunen, P.: Nonlinear Independent Component Analysis: Existence and Uniqueness Results. Neural Networks 12(2), 429–439 (1999)
Solazzi, M., Uncini, A.: Spline Neural Networks for Blind Separation of Post- Nonlinear-Linear Mixtures. IEEE Trans. on Circuits and Systems I Fundamental Theory and Applications 51(4), 817–829 (2004)
Uncini, A., Vecci, L., Piazza, F.: Learning and approximation capabilities of adaptive Spline activation function neural network. NN 11(2), 259–270 (1998)
Milani, F., Solazzi, M., Uncini, A.: Blind Source Separation of convolutive nonlinear mixtures by flexible spline nonlinear functions. In: Proc. of IEEE ICASSP 2002, Orlando, USA (May 2002)
Zade, M.B., Jutten, C., Najeby, K.: Blind Separating, Convolutive Post nonlinear Mixture. In: Proc. of the 3rd Workshop on Independent Component Analysis and Signal Separation (ICA2001), San Diego, California, USA, pp. 138–143 (2001)
Shobben, D., Torkkola, K., Smaragdis, P.: Evaluation of blind signal separation methods. In: Proc. of ICA and BSS, Aussois, France, January 11-15 (1999)
Vigliano, D., Parisi, R., Uncini, A.: A flexible approach to a novel BSS convolutive nonlinear problem: preliminary result. In: Proc. of Italian Workshop on Neural Networks (WIRN 2004), Perugia. Springer, Heidelberg (2004)
Vigliano, D., Parisi, R., Uncini, A.: An Information Theoretic Approach to a Novel Nonlinear Independent Component Analysis Paradigm. Elsevier Signal Processing Special Issue on Information Theoretic (2005) (in press)
Choi, S., Cichocki, A.: Adaptive Blind Separation of speech signals: Cocktail party problem. In: ICSP 1997, Seoul, Korea, August 26-28, pp. 617–622 (1997)
Woo, W.L., Khor, L.C.: Blind restoration of nonlinearly mixed signals using multilayer polynomial neural network. In: IEE Proceedings of Vision, Image and Signal Processing, vol. 151(1), Feburaray 5 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vigliano, D., Parisi, R., Uncini, A. (2006). A Recurrent ICA Approach to a Novel BSS Convolutive Nonlinear Problem. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_9
Download citation
DOI: https://doi.org/10.1007/11731177_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
eBook Packages: Computer ScienceComputer Science (R0)