NDLI: Mixture-based extension of the AR model and its recursive Bayesian identification

Content Provider	IEEE Xplore Digital Library
Author	Smidl, V. Quinn, A.
Copyright Year	1991
Abstract	An extension of the AutoRegressive (AR) model is studied, which allows transformations and distortions on the regressor to be handled. Many important signal processing problems are amenable to this Extended AR (i.e., EAR) model. It is shown that Bayesian identification and prediction of the EAR model can be performed recursively, in common with the AR model itself. The EAR model does, however, require that the transformation be known. When it is unknown, the associated transformation space is represented by a finite set of candidates. What follows is a Mixture-based EAR model, i.e., the MEAR model. An approximate identification algorithm for MEAR is developed, using a restricted Variational Bayes (VB) method. This restores the elegant recursive update of sufficient statistics. The MEAR model is applied to the robust identification of AR processes corrupted by outliers and burst noise, respectively, and to click removal for speech.
Sponsorship	IEEE Signal Processing Society
Starting Page	3530
Ending Page	3542
Page Count	13
File Size	614692
File Format	PDF
ISSN	1053587X
Volume Number	53
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Ear Parametric statistics Predictive models Least squares approximation Signal processing algorithms Statistical distributions Speech analysis Signal processing Noise robustness Variational Bayes Adaptive systems Bayesian identification burst noise conjugacy filterbank mixtures recursive identification sufficient statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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