The Significance-Aware EPFES to Estimate a Memoryless Preprocessor for Nonlinear Acoustic Echo Cancellation

In this article, we introduce a novel approach for estimating the coefficients of a memoryless preprocessor for nonlinear acoustic echo cancellation (NL-AEC) using particle filtering. The acoustic echo path is modeled by a nonlinear-linear cascade of a memoryless preprocessor (to model the loudspeaker nonlinearities) preceding a linear finite impulse response filter (estimated by the normalized least mean square algorithm). For identifying the loudspeaker signal distortions, we follow the concept of significance-aware filtering by modeling the time-variant coefficients of the memoryless preprocessor and the direct-path part of the room impulse response vector as one state vector with non-Gaussian probability distribution. Due to the nonlinear relation between the state vector and the observation, we propose a computationally-efficient realization of the recently published elitist particle filter based on evolutionary strategies (EPFES), which evaluates realizations of the state vector based on long-term fitness measures. The experimental validation comprises predefined loudspeaker signal distortions as well as real recordings stemming from a commercial smartphone. In comparison to the well-known Hammerstein group model for NL-AEC, the computational complexity is reduced and the achievable system identification is improved for both scenarios.