Significance-aware Hammerstein group models for nonlinear acoustic echo cancellation

In this work, a novel approach for nonlinear acoustic echo cancellation is proposed. The main innovative idea of the proposed method is to model only the small region of the echo path around the direct path by a group of parallel Hammerstein models, to estimate a nonlinear preprocessor by correlations between the linear kernels of the Hammerstein submodels, and to describe the remaining echo path by a simple Hammerstein model with the preprocessor determined in the aforementioned way. While the computational complexity of such a system increases only slightly in comparison to a linear echo canceller, experiments with speech recordings from a smartphone in different environments confirm a significantly increased echo cancellation performance.