Efficient nonlinear acoustic echo cancellation by partitioned-block Significance-Aware Hammerstein Group Models

摘要

A powerful and efficient model for nonlinear echo paths of hands-free communication systems is given by the recently proposed Significance-Aware Hammerstein Group Model (SA-HGM). Such a model learns memoryless loudspeaker nonlinearities on a small temporal support of the echo path (preferably the direct-sound region) and extrapolates the nonlinearities for the entire echo path afterwards. In this contribution, an efficient frequency-domain realization of the significance-aware concept for nonlinear acoustic echo cancellation is proposed. The proposed method exploits the benefits of partitioned-block frequency-domain adaptive filtering and will therefore be referred to as Partitioned-Block Significance-Aware Hammerstein Group Model (PBSA-HGM). This allows to efficiently model a long nonlinear echo path by a linear partitioned-block frequency-domain adaptive filter after a parametric memoryless nonlinear preprocessor, the parameters of which are estimated via a nonlinear Hammerstein Group Model (HGM) with the short temporal support of a single block only.