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Modeling and Adaptive Filtering for Systems with Output Nonlinearity

Many practical systems are nonlinear in nature, and the Volterra series, also known as nonlinear convolution, is widely used to model these systems. For nonlinear systems with infinite memory, such a modeling approach is usually not feasible because of multiple infinite summations. In practice, the full Volterra series representation of such a system is either approximated by just a few terms, or is otherwise simplified. In an audio system, a useful approximation is to model all memoryless and dynamical nonlinear effects as a combined nonlinearity at its output. In this paper we propose a new Volterra-based structure that accommodates nonlinear systems with output nonlinearity and infinite memory. We then propose an adaptation approach to estimate the Volterra kernels based on the Least Mean Squares (LMS) approach.

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Permalink: http://www.aes.org/e-lib/browse.cfm?elib=18262

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