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

Hofmann C, Günther M, Hümmer C, Kellermann W (2016)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2016

Pages Range: 1783-1787

Event location: Budapest HU

ISBN: 978-0-9928-6265-7

DOI: 10.1109/EUSIPCO.2016.7760555

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.

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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.

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