Improvement of Convergence Property on Adaptive Wiener Filter Using Stochastic Gradient Adaptive Algorithm

Abstract

An adaptive Volterra filter (AVF) is one of the identification methods to identify Volterra kernels of a target nonlinear system via any adaptive algorithm. However, the convergence speed and the identification accuracy of the AVF may deteriorate in the case of colored input signal. An adaptive Wiener filter (AWF) is one of the solutions to solve the problem of the AVF. Since the AWF guarantees the orthogonality of the Gaussian white noise on each order input signal, the identification accuracy is improved compared with the AVF. However, when the AWF is used for identification of the target nonlinear system, the auto-correlation matrix of each order input signal vector may have different eigenvalues and convergence speed becomes slower. One of the solutions for this problem is stochastic gradient adaptive algorithm. In this paper, we examine the identification ability for loudspeaker systems by the AWF with stochastic gradient adaptive algorithm. Simulation and experiment results demonstrate that the convergence speed can be improved compared with the AVF.