Hidden Markov modeling of noise periodograms using Rayleigh mixture models
(2005) 39th Asilomar Conference on Signals, Systems and Computers, 2005 p.1666-1670- Abstract
- In this paper, we derive an Expectation-Maximization algorithm for hidden Markov models (HMMs) with a multivariate Rayleigh mixture model (RMM) in each state. We compare the use of multivariate RMMs to multivariate Gaussian mixture models in the general case where the HMM is a dynamic model and for the special case where it has a single state and reduces to a static model. We evaluate the proposed method when used to model probability density of periodpgrams from real-life noise sources and white Gaussian noise, which we include for reference purposes.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4092538
- author
- Sorensen, Karsten Vandborg and Andersen, Sören Vang LU
- publishing date
- 2005
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2005 39th Asilomar Conference on Signals, Systems and Computers, Vols 1 and 2
- pages
- 1666 - 1670
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 39th Asilomar Conference on Signals, Systems and Computers, 2005
- conference location
- Pacific Grove, CA, United States
- conference dates
- 2005-10-30 - 2005-11-02
- external identifiers
-
- wos:000238142000321
- scopus:33847673637
- language
- English
- LU publication?
- no
- id
- f4ab7ba6-f9df-42a9-8454-426e953a09ba (old id 4092538)
- date added to LUP
- 2016-04-04 12:06:48
- date last changed
- 2022-01-29 22:57:59
@inproceedings{f4ab7ba6-f9df-42a9-8454-426e953a09ba,
abstract = {{In this paper, we derive an Expectation-Maximization algorithm for hidden Markov models (HMMs) with a multivariate Rayleigh mixture model (RMM) in each state. We compare the use of multivariate RMMs to multivariate Gaussian mixture models in the general case where the HMM is a dynamic model and for the special case where it has a single state and reduces to a static model. We evaluate the proposed method when used to model probability density of periodpgrams from real-life noise sources and white Gaussian noise, which we include for reference purposes.}},
author = {{Sorensen, Karsten Vandborg and Andersen, Sören Vang}},
booktitle = {{2005 39th Asilomar Conference on Signals, Systems and Computers, Vols 1 and 2}},
language = {{eng}},
pages = {{1666--1670}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Hidden Markov modeling of noise periodograms using Rayleigh mixture models}},
year = {{2005}},
}