DocumentCode :
177902
Title :
The elitist particle filter based on evolutionary strategies as novel approach for nonlinear acoustic echo cancellation
Author :
Huemmer, Christian ; Hofmann, C. ; Maas, R. ; Schwarz, Andreas ; Kellermann, Walter
Author_Institution :
Multimedia Commun. & Signal Process., Univ. of Erlangen-Nuremberg, Erlangen, Germany
fYear :
2014
fDate :
4-9 May 2014
Firstpage :
1315
Lastpage :
1319
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.
Keywords :
acoustic signal processing; echo suppression; evolutionary computation; particle filtering (numerical methods); probability; elitist particle filter; evolutionary strategies; modified particle filter; nonGaussian probability distribution; nonlinear acoustic echo cancellation; nonlinear echo path stemming; numerical sampling method; particle filtering; Approximation methods; Bayes methods; Echo cancellers; Nonlinear acoustics; Speech; Vectors; Echo cancellation; evolutionary strategies; nonlinear AEC; particle filter; system identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
Type :
conf
DOI :
10.1109/ICASSP.2014.6853810
Filename :
6853810
Link To Document :
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