On Bayesian networks in speech signal processing

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Maas R, Hümmer C, Hofmann C, Kellermann W
Publisher: VDE
Publication year: 2014
Pages range: 1-4
ISBN: 978-3-8007-3640-9
Language: English


Abstract


This paper describes a class of relevant speech signal processing algorithms as probabilistic inference problems. Starting with an observation model that relates all involved random variables, we convert the respective joint probability density function into its Bayesian network representation in order to infer the desired signal estimates. After recalling the well-known Bayesian network descriptions of Wiener filtering and adaptive filtering, we show how the proportionate normalized least mean square (PNLMS) algorithm arises under certain restrictive assumptions on the covariance matrices of the latent random variables. In this context, the inherent relation of the Kalman filter, the normalized least mean square (NLMS), and the PNLMS algorithm is moreover outlined. We finally recall that also unsupervised signal estimation problems, such as dereverberation, can be considered from the same point of view.


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


How to cite

APA:
Maas, R., Hümmer, C., Hofmann, C., & Kellermann, W. (2014). On Bayesian networks in speech signal processing. In Proceedings of the 11. ITG Symposium on Speech Communication (pp. 1-4). Erlangen, DE: VDE.

MLA:
Maas, Roland, et al. "On Bayesian networks in speech signal processing." Proceedings of the 11. ITG Symposium on Speech Communication, Erlangen VDE, 2014. 1-4.

BibTeX: 

Last updated on 2019-20-04 at 19:08