--- _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' ...