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Wavelet -Based Statistical Signal Processing using Hidden Markov Models

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Title: Wavelet -Based Statistical Signal Processing using Hidden Markov Models
Author: Crouse, Matthew; Nowak, Robert David; Baraniuk, Richard G.
Type: Journal Paper
Keywords: hidden Markov models (HMMs); Expectation Maximization (EM); Gaussian
Citation: M. Crouse, R. D. Nowak and R. G. Baraniuk, "Wavelet -Based Statistical Signal Processing using Hidden Markov Models," IEEE Transactions on Signal Processing, vol. 46, no. 4, pp. 886-902, 1998.
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 (HMMs). The framework enables us to concisely model the statistical dependencies and non-Gaussian Statistics encountered with real-world signals. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable probabilistic signal modes. 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, classificaion, and detection.
Date Published: 1998-04-01

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  • ECE Publications [1032 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.