A Bayesian network view on linear and nonlinear acoustic echo cancellation

Conference contribution
(Conference Contribution)

Publication Details

Author(s): Maas R, Hümmer C, Schwarz A, Hofmann C, Kellermann W
Publication year: 2014
Pages range: 495-499
ISBN: 978-1-4799-5403-2
Language: English


In this contribution, we provide a new derivation of the normalized least mean square (NLMS) algorithm from a machine learning perspective. By applying the inference rules of Bayesian networks to a linear observation model, the NLMS can be shown to arise as a modification of the Kalman filter equations. Based on a nonlinear observationmodel, we exemplify the benefit of the Bayesian point of view by employing the technique of particle filtering to realize a tractable algorithm for nonlinear acoustic echo cancellation. Experiments carried out on real smartphone recordings reveal the remarkable performance of the new approach.

FAU Authors / FAU Editors

Hofmann, Christian
Professur für Nachrichtentechnik
Hümmer, Christian
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Maas, Roland
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Schwarz, Andreas
Professur für Nachrichtentechnik

How to cite

Maas, R., Hümmer, C., Schwarz, A., Hofmann, C., & Kellermann, W. (2014). A Bayesian network view on linear and nonlinear acoustic echo cancellation. (pp. 495-499). Xi'an, CN.

Maas, Roland, et al. "A Bayesian network view on linear and nonlinear acoustic echo cancellation." Proceedings of the 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014, Xi'an 2014. 495-499.


Last updated on 2018-03-12 at 20:53