Wavelet-Based Statistical Signal Processing Using Hidden Markov Models

Wavelet-Based Statistical Signal Processing Using Hidden Markov Models (1998)

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by Matthew Crouse , Robert Nowak , Richard Baraniuk

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Datum	Value	Source
TITLE	Wavelet-Based Statistical Signal Processing Using Hidden Markov Models	user correction - Legacy Corrections
AUTHOR NAME	Matthew Crouse	SVM HeaderParse 0.1
AUTHOR AFFIL	\Pi	SVM HeaderParse 0.2
AUTHOR NAME	Robert Nowak	SVM HeaderParse 0.1
AUTHOR AFFIL	2	SVM HeaderParse 0.2
AUTHOR NAME	Richard Baraniuk	SVM HeaderParse 0.1
AUTHOR AFFIL	; Department of Electrical and Computer Engineering; Rice University; 2; Department of Electrical Engineering; Michigan State University	SVM HeaderParse 0.2
AUTHOR ADDR	; 6100 South Main Street; Houston, TX 77005--1892; 260 Engineering Building; East Lansing, MI 48824--1226	SVM HeaderParse 0.1
ABSTRACT	Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the statistical dependencies and nonGaussian statistics often encountered in practice. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable probabilistic signal models. Efficient Expectation Maximization algorithms are developed for fitting the HMMs to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMMs, we develop novel algorithms for signal denoising, classification, and detectio...	user correction - Legacy Corrections
YEAR	1998	user correction - Legacy Corrections
CITATIONS	34 found	ParsCit 1.0