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Online blind separation of non stationary signals

This paper addresses the problem of blind separation of non stationary signals. We introduce an online separating algorithm for estimation of independent source signals using the assumption of non-stationarity of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter. | Yugoslav Journal of Operations Research 15 (2005), Number 1, 79-95 ON-LINE BLIND SEPARATION OF NON-STATIONARY SIGNALS Slavica TODOROVIĆ-ZARKULA EI “Professional Electronics”, Niš, bssmtod@eunet.yu Branimir TODOROVIĆ, Miomir STANKOVIĆ Faculty of Occupational Safety, Niš {todor,mstan}@znrfak.znrfak.ni.ac.yu Presented at XXX Yugoslav Simposium on Operations Research Received: January 2004 / Accepted: January 2005 Abstract: This paper addresses the problem of blind separation of non-stationary signals. We introduce an on-line separating algorithm for estimation of independent source signals using the assumption of non-stationarity of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter. Simulation results obtained in blind separation of artificial and real-world signals from their artificial mixtures have shown that separating algorithm based on the extended Kalman filter outperforms stochastic gradient based algorithm both in convergence speed and estimation accuracy. Keywords: Blind source separation, decorrelaton, neural networks, extended Kalman filter. 1. INTRODUCTION Blind separation of sources refers to the problem of recovering source signals from their instantaneous mixtures using only the observed mixtures. The separation is called blind, because it assumes very weak assumptions on source signals and the mixing process. The key assumption is the statistical independence of source signals. A goal is to obtain output signals that are as independent as possible using the observed mixture signals. 80 S. Todorović-Zarkula, B. Todorović, M. Stanković / On-Line Blind Separation In the last few years, the problem of blind source separation has received considerable attention. Since .