---
_id: '11740'
abstract:
- lang: eng
text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of
the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations.
We assume the distortion to be white Gaussian noise of known mean and variance.
An approximate conjugate prior of the GMM parameters is derived allowing for a
computationally efficient implementation in a sequential estimation framework.
Simulations on artificially generated data demonstrate the superiority of the
proposed method compared to the Maximum Likelihood technique and to the ordinary
MAP approach, whose estimates are corrected by the known statistics of the distortion
in a straightforward manner.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian
Mixture Model in the Presence of Noisy Observations. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:3352-3356.
doi:10.1109/ICASSP.2013.6638279'
apa: Chinaev, A., & Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations. In 38th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)
(pp. 3352–3356). https://doi.org/10.1109/ICASSP.2013.6638279
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of
the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations},
DOI={10.1109/ICASSP.2013.6638279},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013},
pages={3352–3356} }'
chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.”
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 3352–56, 2013. https://doi.org/10.1109/ICASSP.2013.6638279.
ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of
a Gaussian Mixture Model in the Presence of Noisy Observations,” in 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–3356.
mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations.” 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–56, doi:10.1109/ICASSP.2013.6638279.
short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.'
date_created: 2019-07-12T05:27:20Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638279
keyword:
- Gaussian noise
- maximum likelihood estimation
- parameter estimation
- GMM parameter
- Gaussian mixture model
- MAP estimation
- Map-based estimation
- maximum a-posteriori estimation
- maximum likelihood technique
- noisy observation
- sequential estimation framework
- white Gaussian noise
- Additive noise
- Gaussian mixture model
- Maximum likelihood estimation
- Noise measurement
- Gaussian mixture model
- Maximum a posteriori estimation
- Maximum likelihood estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf
oa: '1'
page: 3352-3356
publication: 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf
status: public
title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence
of Noisy Observations
type: conference
user_id: '44006'
year: '2013'
...