A Variational Bayesian Learning Approach for Nonlinear Acoustic Echo Control

In this work, we present novel Bayesian algorithms for acoustic echo cancellation and residual echo suppression in the presence of a memoryless loudspeaker nonlinearity. The system nonlinearity is modeled using a basis-generic nonlinear expansion. This allows us to express the microphone observation...

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Vydáno v:	IEEE transactions on signal processing Ročník 61; číslo 23; s. 5853 - 5867
Hlavní autoři:	Malik, Sarmad, Enzner, Gerald
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.12.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Acoustics Adaptation models Algorithms Applied sciences Bayes methods Bayesian analysis Cancellation Cascade modeling Detection, estimation, filtering, equalization, prediction Exact sciences and technology Information, signal and communications theory Learning Mathematical analysis Mathematical model Mathematical models Miscellaneous nonlinear echo cancellation Nonlinearity Operations research post-filtering Random variables recursive Bayesian estimator Signal and communications theory Signal processing Signal, noise State-space methods state-space model Telecommunications and information theory Vectors Vectors (mathematics) Cascade modeling Microphone Variational methods Noise reduction Discrete Fourier transformation Loudspeaker Non linear phenomenon Composite material Learning Acoustic noise post-filtering Random variable Lower bound Probabilistic approach Acoustic signal recursive Bayesian estimator state-space model nonlinear echo cancellation Markov model State space method Acoustic signal processing Echo suppression Algorithm Residual impurity First order A priori estimation Signal processing Bayes methods Non linear effect
ISSN:	1053-587X, 1941-0476
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Popis
Shrnutí:	In this work, we present novel Bayesian algorithms for acoustic echo cancellation and residual echo suppression in the presence of a memoryless loudspeaker nonlinearity. The system nonlinearity is modeled using a basis-generic nonlinear expansion. This allows us to express the microphone observation in the DFT domain in terms of the nonlinear-expansion coefficients and the acoustic echo path. We augment the observation model with first-order Markov models for the echo-path vector and the nonlinear-expansion coefficients to arrive at a composite state-space model. The echo path vector and each nonlinear-expansion coefficient are designated as the unknown random variables in our Bayesian model. The posterior estimators for the random variables and the learning rules for the a priori unknown model parameters are then derived via the maximization of the variational lower bound on the log likelihood. We further show that a Bayesian post-filter for residual echo suppression can be derived by optimizing a minimum-mean-square error (MMSE) cost function subject to marginalization with respect to the posteriors estimated in the echo cancellation stage. The effectiveness of the approach is supported by simulation results and an analysis using instrumental performance measures.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2013.2281021