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Wavelet-based statistical signal processing using hidden Markov models (1998)
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| Venue: | IEEE TRANSACTIONS ON SIGNAL PROCESSING |
| Citations: | 415 - 50 self |
BibTeX
@ARTICLE{Crouse98wavelet-basedstatistical,
author = {Matthew S. Crouse and Robert D. Nowak and Richard G. Baraniuk},
title = {Wavelet-based statistical signal processing using hidden Markov models},
journal = {IEEE TRANSACTIONS ON SIGNAL PROCESSING},
year = {1998},
volume = {46},
number = {4},
pages = {886--902}
}
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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 for statistical signal processing based on wavelet-domain hidden Markov models (HMM’s) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals. Wavelet-domain HMM’s 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 HMM’s 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 HMM’s, we develop novel algorithms for signal denoising, classification, and detection.
Keyphrases
wavelet-based statistical signal processing hidden markov model new framework wavelet-domain hmm index term hidden markov model wide range non-gaussian statistic real-world signal intrinsic property abstract wavelet-based statistical signal observational signal data statistical dependency statistical signal processing probabilistic graph efficient expectation maximization algorithm probabilistic signal model signal denoising novel algorithm many real-world signal wavelet-domain hidden markov model wavelet coefficient signal estimation wavelet transform
