The elitist particle filter based on evolutionary strategies as novel approach for nonlinear acoustic echo cancellation

Abstract

In this article, we introduce a novel approach for nonlinear acoustic echo cancellation based on a combination of particle filtering and evolutionary strategies. The nonlinear echo path is modeled as a state vector with non-Gaussian probability distribution and the relation to the observed signals and near-end interferences are captured by nonlinear functions. To estimate the probability distribution of the state vector and the model parameters, we apply the numerical sampling method of particle filtering, where each set of particles represents different realizations of the nonlinear echo path. While the classical particle-filter approach is unsuitable for system identification with large search spaces, we introduce a modified particle filter to select elitist particles based on long-term fitness measures and to create new particles based on the approximated probability distribution of the state vector. The validity of the novel approach is experimentally verified with real recordings for a nonlinear echo path stemming from a commercial smartphone.