By Topic

Signal denoising using wavelet and block hidden Markov model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Liao, Z.W. ; Dept. of Comput. Sci., Hong Kong Baptist Univ., Kowloon, China ; Lam, E.C.M. ; Tang, Y.Y.

In this paper, we propose a novel wavelet domain HMM using block to strike a delicate balance between improving spatial adaptability of contextual HMM (CHMM) and modeling a more reliable HMM. Each wavelet coefficient is modeled as a Gaussian mixture model, and the dependencies among wavelet coefficients in each subband are described by a context structure, then the structure is modified by blocks which are connected areas in a scale conditioned on the same context. Before denoising the signal, efficient expectation maximization (EM) algorithms are developed for fitting the HMMs to observational signal data. Parameters of trained HMM are used to modify wavelet coefficients according to the rule of minimizing the mean squared error (MSE) of the signal. Then, the reverse wavelet transformation is utilized to modify wavelet coefficients. Finally, experimental results are given. The results show that the block hidden Markov model (BHMM) is a powerful yet simple tool in signal denoising.

Published in:

Machine Learning and Cybernetics, 2003 International Conference on  (Volume:4 )

Date of Conference:

2-5 Nov. 2003