Rayleigh mixture model-based hidden Markov modeling and estimation of noise in noisy speech signals
(2007) In IEEE Transactions on Audio, Speech, and Language Processing 15(3). p.901-917- Abstract
- In this paper, we propose a new statistical model for noise periodogram modeling and estimation. The proposed model is a hidden Markov model (HMM) with a Rayleigh mixture model (RMM) in each state. For this new model, we derive an expectation-maxinuzation (EM) training algorithm and a minimum meansquare error (NIMSE) noise periodogram estimator. It is shown that when compared to the Gaussian mixture model (GMM)-based HMM, the RMM-based HMM has less computationally complex EM iterations and gives a better fit of the noise periodograms when the mixture models has a low number of components. Furthermore, we propose a specialization of the proposed model, which is shown to provide better MMSE noise periodograrn estimates than any other of the... (More)
- In this paper, we propose a new statistical model for noise periodogram modeling and estimation. The proposed model is a hidden Markov model (HMM) with a Rayleigh mixture model (RMM) in each state. For this new model, we derive an expectation-maxinuzation (EM) training algorithm and a minimum meansquare error (NIMSE) noise periodogram estimator. It is shown that when compared to the Gaussian mixture model (GMM)-based HMM, the RMM-based HMM has less computationally complex EM iterations and gives a better fit of the noise periodograms when the mixture models has a low number of components. Furthermore, we propose a specialization of the proposed model, which is shown to provide better MMSE noise periodograrn estimates than any other of the tested HMA4 initializations for cyclo-stationary noise types. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4092498
- author
- Sorensen, Karsten Vandborg and Andersen, Sören Vang LU
- publishing date
- 2007
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- hidden Markov model (HMM), probability density function, Rayleigh, mixture model (RMM), speech enhancement
- in
- IEEE Transactions on Audio, Speech, and Language Processing
- volume
- 15
- issue
- 3
- pages
- 901 - 917
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000244318600015
- scopus:64149128862
- ISSN
- 1558-7924
- DOI
- 10.1109/TASL.2006.885240
- language
- English
- LU publication?
- no
- id
- ae9d69e3-26f0-4366-a8e2-eff5fe7f65e2 (old id 4092498)
- date added to LUP
- 2016-04-01 11:38:42
- date last changed
- 2022-03-28 00:58:43
@article{ae9d69e3-26f0-4366-a8e2-eff5fe7f65e2, abstract = {{In this paper, we propose a new statistical model for noise periodogram modeling and estimation. The proposed model is a hidden Markov model (HMM) with a Rayleigh mixture model (RMM) in each state. For this new model, we derive an expectation-maxinuzation (EM) training algorithm and a minimum meansquare error (NIMSE) noise periodogram estimator. It is shown that when compared to the Gaussian mixture model (GMM)-based HMM, the RMM-based HMM has less computationally complex EM iterations and gives a better fit of the noise periodograms when the mixture models has a low number of components. Furthermore, we propose a specialization of the proposed model, which is shown to provide better MMSE noise periodograrn estimates than any other of the tested HMA4 initializations for cyclo-stationary noise types.}}, author = {{Sorensen, Karsten Vandborg and Andersen, Sören Vang}}, issn = {{1558-7924}}, keywords = {{hidden Markov model (HMM); probability density function; Rayleigh; mixture model (RMM); speech enhancement}}, language = {{eng}}, number = {{3}}, pages = {{901--917}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Transactions on Audio, Speech, and Language Processing}}, title = {{Rayleigh mixture model-based hidden Markov modeling and estimation of noise in noisy speech signals}}, url = {{http://dx.doi.org/10.1109/TASL.2006.885240}}, doi = {{10.1109/TASL.2006.885240}}, volume = {{15}}, year = {{2007}}, }