--- _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' ... --- _id: '11816' abstract: - lang: eng text: In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms. author: - first_name: Manh Kha full_name: Hoang, Manh Kha last_name: Hoang - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013). ; 2013:3721-3725. doi:10.1109/ICASSP.2013.6638353' apa: Hoang, M. K., & Haeb-Umbach, R. (2013). Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) (pp. 3721–3725). https://doi.org/10.1109/ICASSP.2013.6638353 bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}, DOI={10.1109/ICASSP.2013.6638353}, booktitle={38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013}, pages={3721–3725} }' chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 3721–25, 2013. https://doi.org/10.1109/ICASSP.2013.6638353. ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning,” in 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725. mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–25, doi:10.1109/ICASSP.2013.6638353. short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.' date_created: 2019-07-12T05:28:48Z date_updated: 2022-01-06T06:51:09Z department: - _id: '54' doi: 10.1109/ICASSP.2013.6638353 keyword: - Gaussian processes - Global Positioning System - convergence - expectation-maximisation algorithm - fingerprint identification - indoor radio - signal classification - wireless LAN - EM algorithm - ML estimation - WiFi indoor positioning - censored Gaussian data classification - clipped data - convergence properties - expectation maximization algorithm - fingerprinting method - maximum likelihood estimation - optimal classification - parameters estimation - portable devices sensitivity - signal strength measurements - wireless LAN positioning systems - Convergence - IEEE 802.11 Standards - Maximum likelihood estimation - Parameter estimation - Position measurement - Training - Indoor positioning - censored data - expectation maximization - signal strength - wireless LAN language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf oa: '1' page: 3721-3725 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/HoHa2013_Poster.pdf status: public title: Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning type: conference user_id: '44006' year: '2013' ... --- _id: '11745' abstract: - lang: eng text: In this paper we present a novel noise power spectral density tracking algorithm and its use in single-channel speech enhancement. It has the unique feature that it is able to track the noise statistics even if speech is dominant in a given time-frequency bin. As a consequence it can follow non-stationary noise superposed by speech, even in the critical case of rising noise power. The algorithm requires an initial estimate of the power spectrum of speech and is thus meant to be used as a postprocessor to a first speech enhancement stage. An experimental comparison with a state-of-the-art noise tracking algorithm demonstrates lower estimation errors under low SNR conditions and smaller fluctuations of the estimated values, resulting in improved speech quality as measured by PESQ scores. author: - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Alexander full_name: Krueger, Alexander last_name: Krueger - first_name: Dang Hai full_name: Tran Vu, Dang Hai last_name: Tran Vu - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Chinaev A, Krueger A, Tran Vu DH, Haeb-Umbach R. Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In: 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012). ; 2012.' apa: Chinaev, A., Krueger, A., Tran Vu, D. H., & Haeb-Umbach, R. (2012). Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012). bibtex: '@inproceedings{Chinaev_Krueger_Tran Vu_Haeb-Umbach_2012, title={Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor}, booktitle={37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)}, author={Chinaev, Aleksej and Krueger, Alexander and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2012} }' chicago: Chinaev, Aleksej, Alexander Krueger, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.” In 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012. ieee: A. Chinaev, A. Krueger, D. H. Tran Vu, and R. Haeb-Umbach, “Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor,” in 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012. mla: Chinaev, Aleksej, et al. “Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.” 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012. short: 'A. Chinaev, A. Krueger, D.H. Tran Vu, R. Haeb-Umbach, in: 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.' date_created: 2019-07-12T05:27:26Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' keyword: - MAP parameter estimation - noise power estimation - speech enhancement language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12.pdf oa: '1' publication: 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012) related_material: link: - description: Presentation relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12_Talk.pdf status: public title: Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor type: conference user_id: '44006' year: '2012' ... --- _id: '11723' abstract: - lang: eng text: In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding. author: - first_name: Maik full_name: Bevermeier, Maik last_name: Bevermeier - first_name: Sven full_name: Peschke, Sven last_name: Peschke - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Bevermeier M, Peschke S, Haeb-Umbach R. Robust vehicle localization based on multi-level sensor fusion and online parameter estimation. In: 6th Workshop on Positioning Navigation and Communication (WPNC 2009). ; 2009:235-242. doi:10.1109/WPNC.2009.4907833' apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Robust vehicle localization based on multi-level sensor fusion and online parameter estimation. In 6th Workshop on Positioning Navigation and Communication (WPNC 2009) (pp. 235–242). https://doi.org/10.1109/WPNC.2009.4907833 bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}, DOI={10.1109/WPNC.2009.4907833}, booktitle={6th Workshop on Positioning Navigation and Communication (WPNC 2009)}, author={Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009}, pages={235–242} }' chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Robust Vehicle Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.” In 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 235–42, 2009. https://doi.org/10.1109/WPNC.2009.4907833. ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Robust vehicle localization based on multi-level sensor fusion and online parameter estimation,” in 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–242. mla: Bevermeier, Maik, et al. “Robust Vehicle Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.” 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–42, doi:10.1109/WPNC.2009.4907833. short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–242.' date_created: 2019-07-12T05:27:01Z date_updated: 2022-01-06T06:51:07Z department: - _id: '54' doi: 10.1109/WPNC.2009.4907833 keyword: - covariance matrices - expectation-maximisation algorithm - expectation-maximization algorithm - global positioning system - Global Positioning System - GPS - inertial measurement unit - interacting multiple model approach - Kalman filters - multilevel sensor fusion - narrow street canyons - narrow tunnels - online parameter estimation - parameter estimation - road vehicles - robust vehicle localization - sensor fusion - state noise covariances - time-variant multilevel Kalman filter - vehicle tracking algorithm language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf oa: '1' page: 235-242 publication: 6th Workshop on Positioning Navigation and Communication (WPNC 2009) status: public title: Robust vehicle localization based on multi-level sensor fusion and online parameter estimation type: conference user_id: '44006' year: '2009' ... --- _id: '11724' abstract: - lang: eng text: In this paper we present a novel vehicle tracking method which is based on multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman filtering of GPS and IMU measurements the estimates of the orientation of the vehicle are combined in an optimal manner to improve the robustness towards drift errors. The tracking algorithm incorporates the estimation of time-variant covariance parameters by using an iterative block Expectation-Maximization algorithm to account for time-variant driving conditions and measurement quality. The proposed system is compared to an interacting multiple model approach (IMM) and achieves improved localization accuracy at lower computational complexity. Furthermore we show how the joint parameter estimation and localizaiton can be conducted with streaming input data to be able to track vehicles in a real driving environment. author: - first_name: Maik full_name: Bevermeier, Maik last_name: Bevermeier - first_name: Sven full_name: Peschke, Sven last_name: Peschke - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Bevermeier M, Peschke S, Haeb-Umbach R. Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning. In: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring). ; 2009:1-5. doi:10.1109/VETECS.2009.5073634' apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning. In IEEE 69th Vehicular Technology Conference (VTC 2009 Spring) (pp. 1–5). https://doi.org/10.1109/VETECS.2009.5073634 bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning}, DOI={10.1109/VETECS.2009.5073634}, booktitle={IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}, author={Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009}, pages={1–5} }' chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.” In IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 1–5, 2009. https://doi.org/10.1109/VETECS.2009.5073634. ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning,” in IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 2009, pp. 1–5. mla: Bevermeier, Maik, et al. “Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.” IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 2009, pp. 1–5, doi:10.1109/VETECS.2009.5073634. short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 2009, pp. 1–5.' date_created: 2019-07-12T05:27:02Z date_updated: 2022-01-06T06:51:07Z department: - _id: '54' doi: 10.1109/VETECS.2009.5073634 keyword: - computational complexity - expectation-maximisation algorithm - Global Positioning System - inertial measurement unit - inertial navigation - interacting multiple model - iterative block expectation-maximization algorithm - Kalman filters - multi-stage Kalman filter - parameter estimation - road vehicles - vehicle positioning - vehicle tracking language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf oa: '1' page: 1-5 publication: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring) status: public title: Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning type: conference user_id: '44006' year: '2009' ... --- _id: '11938' abstract: - lang: eng text: In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise. author: - first_name: Stefan full_name: Windmann, Stefan last_name: Windmann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: Windmann S, Haeb-Umbach R. Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing. 2009;17(8):1577-1590. doi:10.1109/TASL.2009.2023172 apa: Windmann, S., & Haeb-Umbach, R. (2009). Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing, 17(8), 1577–1590. https://doi.org/10.1109/TASL.2009.2023172 bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition}, volume={17}, DOI={10.1109/TASL.2009.2023172}, number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={1577–1590} }' chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition.” IEEE Transactions on Audio, Speech, and Language Processing 17, no. 8 (2009): 1577–90. https://doi.org/10.1109/TASL.2009.2023172.' ieee: S. Windmann and R. Haeb-Umbach, “Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 8, pp. 1577–1590, 2009. mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 8, 2009, pp. 1577–90, doi:10.1109/TASL.2009.2023172. short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 1577–1590. date_created: 2019-07-12T05:31:09Z date_updated: 2022-01-06T06:51:12Z department: - _id: '54' doi: 10.1109/TASL.2009.2023172 intvolume: ' 17' issue: '8' keyword: - AURORA4 database - blockwise EM algorithm - covariance analysis - linear state model - noise covariance - noise-robust automatic speech recognition - noisy speech cepstra - offline training mode - parameter estimation - speech recognition - speech recognition equipment - speech recognizer - state-space methods - state-space model language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-2.pdf oa: '1' page: 1577-1590 publication: IEEE Transactions on Audio, Speech, and Language Processing status: public title: Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition type: journal_article user_id: '44006' volume: 17 year: '2009' ...