Conference contribution
(Conference Contribution)


A Bayesian network viewon linear and nonlinear acoustic echo cancellation


Publication Details
Author(s): Maas R, Hümmer C, Schwarz A, Hofmann C, Kellermann W
Publisher: Institute of Electrical and Electronics Engineers Inc.
Publication year: 2014
Pages range: 495-499
ISBN: 9781479954032

Event details
Event: 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014
Event location: Xi'an
Start date of the event: 09/07/2014
End date of the event: 13/07/2014

Abstract

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.



How to cite
APA: Maas, R., Hümmer, C., Schwarz, A., Hofmann, C., & Kellermann, W. (2014). A Bayesian network viewon linear and nonlinear acoustic echo cancellation. (pp. 495-499). Xi'an, CN: Institute of Electrical and Electronics Engineers Inc..

MLA: Maas, Roland, et al. "A Bayesian network viewon 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 Institute of Electrical and Electronics Engineers Inc., 2014. 495-499.

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