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

A Bayesian network approach to linear and nonlinear acoustic echo cancellation

“…In many practical applications, nonlinear loudspeaker signal distortions created by amplifiers and transducers in minituarized loudspeakers prior to the linear acoustic impulse response limit the practical performance of linear echo path models. This justifies to model the overall echo path by a nonlinear-linear cascade of a memoryless preprocessor (to model nonlinear loudspeaker signal distortions) preceding the linear FIR filterĥ n (to model the sound propagation through air) [9,16,17], see Fig. 4.…”

“…These properties of the classical particle filter are severe limitations for the task of nonlinear AEC with its high-dimensional state vector (see (22)). To cope with these conceptional limitations without introducing sophisticated resampling methods [40,44], the elitist particle filter based on evolutionary strategies (EPFES) has been recently proposed in [9]. As major modifications for the task of nonlinear AEC, an evolutionary selection process facilitates to evaluate realizations of the state vector based on recursively calculated particle weights to generalize the instantaneous solution of the optimization problem [9].…”

“…To cope with these conceptional limitations without introducing sophisticated resampling methods [40,44], the elitist particle filter based on evolutionary strategies (EPFES) has been recently proposed in [9]. As major modifications for the task of nonlinear AEC, an evolutionary selection process facilitates to evaluate realizations of the state vector based on recursively calculated particle weights to generalize the instantaneous solution of the optimization problem [9]. These fundamental properties of the EPFES will be illustrated for the state-space model of (7) in the next part.…”

“…Despite these challenges, single-channel linear AEC has already reached a mature state as vital part of modern communication *Correspondence: huemmer@lnt.de Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, Cauerstraße 7, Erlangen, Germany devices. On the other hand, the nonlinear distortions created by amplifiers and transducers in miniaturized loudspeakers require dedicated nonlinear echo-path models and are still a very active research topic [8,9]. In this context, a variety of concepts for nonlinear AEC have been proposed based on artificial neural networks [10,11], Volterra filters [12,13], or Kernel methods [14,15].…”

“…In this context, a variety of concepts for nonlinear AEC have been proposed based on artificial neural networks [10,11], Volterra filters [12,13], or Kernel methods [14,15]. A commonly used model, which is also considered in this article, is a cascade of a nonlinear memoryless preprocessor (to model the loudspeaker signal distortions) and an adaptive linear FIR filter (to model the acoustic sound propagation and the microphone) [9,[16][17][18][19].…”