Iterated Coefficient Updates of Partitioned Block Frequency Domain Second-Order Volterra Filters for Nonlinear AEC

This paper presents the benefits of iterated coefficient updates for the adaptation of partitioned block frequency domain second-order Volterra filters when applied to nonlinear acoustic echo cancellation. In order to increase the convergence speed of an NLMS algorithm with separate kernel normalization, each input frame is used for several coefficient updates. This procedure effectively accelerates the convergence of the employed adaptive Volterra filters and is shown to be superior to processing with increased data overlap. The advantages of this novel approach are illustrated by experimental results for noise and speech input and guidelines for determining suitable numbers of iterations for the filter kernels are given.