Efficient nonlinear acoustic echo cancellation by partitioned-block significance-aware Hammerstein group models

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Hofmann C, Günther M, Hümmer C, Kellermann W
Publication year: 2016
Pages range: 1783-1787
ISBN: 978-0-9928-6265-7
ISSN: 2076-1465
Language: English


Abstract

A powerful and efficient model for nonlinear echo paths of hands-free communication systems is given by the recently proposed Significance-Aware Hammerstein Group Model (SA-HGM). Such a model learns memoryless loudspeaker nonlinearities on a small temporal support of the echo path (preferably the direct-sound region) and extrapolates the nonlinearities for the entire echo path afterwards. In this contribution, an efficient frequency-domain realization of the significance-aware concept for nonlinear acoustic echo cancellation is proposed. The proposed method exploits the benefits of partitioned-block frequency-domain adaptive filtering and will therefore be referred to as Partitioned-Block Significance-Aware Hammerstein Group Model (PBSA-HGM). This allows to efficiently model a long nonlinear echo path by a linear partitioned-block frequency-domain adaptive filter after a parametric memoryless nonlinear preprocessor, the parameters of which are estimated via a nonlinear Hammerstein Group Model (HGM) with the short temporal support of a single block only.


FAU Authors / FAU Editors

Günther, Michael
Professur für Nachrichtentechnik
Hofmann, Christian
Professur für Nachrichtentechnik
Hümmer, Christian
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Hofmann, C., Günther, M., Hümmer, C., & Kellermann, W. (2016). Efficient nonlinear acoustic echo cancellation by partitioned-block significance-aware Hammerstein group models. In Proceedings of the European Signal Processing Conf. (EUSIPCO) (pp. 1783-1787). Budapest, HU.

MLA:
Hofmann, Christian, et al. "Efficient nonlinear acoustic echo cancellation by partitioned-block significance-aware Hammerstein group models." Proceedings of the European Signal Processing Conf. (EUSIPCO), Budapest 2016. 1783-1787.

BibTeX: 

Last updated on 2019-18-04 at 21:10