DocumentCode :
2804050
Title :
Online maximum-likelihood learning of time-varying dynamical models in block-frequency-domain
Author :
Malik, Sarmad ; Enzner, Gerald
Author_Institution :
Inst. of Commun. Acoust., Ruhr-Univ. Bochum, Bochum, Germany
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
3822
Lastpage :
3825
Abstract :
A linear dynamical model can be used to describe the evolution of an unknown system in noisy conditions. However, in most applications model parameters of a dynamical system are not known a priori, bringing into question the optimality of traditional state-only estimators. In this paper, we consider block-frequency-domain dynamical models and formulate an optimal framework for low-latency joint state and parameter estimation. We show that the resulting variational expectation-maximization algorithm in the block-frequency-domain offers a comprehensive and efficient solution for the joint estimation task.
Keywords :
Kalman filters; expectation-maximisation algorithm; frequency-domain analysis; signal processing; block-frequency-domain; linear dynamical model; low-latency joint state; online maximum-likelihood learning; parameter estimation; state-only estimators; time-varying dynamical models; Acoustics; Adaptive filters; Adaptive signal processing; Expectation-maximization algorithms; Maximum likelihood estimation; Parameter estimation; Signal processing algorithms; State estimation; System identification; Time varying systems; State-space model; frequency-domain adaptive filtering; maximum likelihood; variational optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495841
Filename :
5495841
Link To Document :
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