Relative impulse response estimation during doubletalk with an artificial neural network-based step size control

Conference contribution


Publication Details

Author(s): Meier S, Kellermann W
Publication year: 2016
Pages range: 1-5
ISBN: 978-1-5090-2007-2
Language: English


Abstract

The Normalized Least-Mean Squares (NLMS) algorithm is a widely used
method for linear system identification (e.g., for Acoustic Echo
Cancellation (AEC), where the acoustic path between loudspeaker and
microphone needs to be estimated). As soon as interferers or background
noise are active, step size control becomes a crucial task in order to
ensure a fast but stable adaptation. Conventional step size control
methods address the case of additive noise contaminating the system
output, i.e., microphone signal. When the NLMS algorithm is used for
Relative Impulse Response (RIR) estimation, however, both the input
signal and the output signal of the unknown system are noisy (since both
of them are microphone signals) and a different step size control is
required. In this paper, the derivation of a new step size is presented.
Since the resulting step size cannot be determined directly, an
Artificial Neural Network (ANN)-based estimation of the step size is
proposed and its effectiveness is demonstrated.


FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Meier, Stefan
Professur für Nachrichtentechnik


How to cite

APA:
Meier, S., & Kellermann, W. (2016). Relative impulse response estimation during doubletalk with an artificial neural network-based step size control. In Proceedings of the International Workshop on Acoustic Signal Enhancement (IWAENC) (pp. 1-5). Xi'an, CN.

MLA:
Meier, Stefan, and Walter Kellermann. "Relative impulse response estimation during doubletalk with an artificial neural network-based step size control." Proceedings of the International Workshop on Acoustic Signal Enhancement (IWAENC), Xi'an 2016. 1-5.

BibTeX: 

Last updated on 2019-19-04 at 12:53