[{"oa":"1","citation":{"ieee":"M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning,” in <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3721–3725.","apa":"Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i> (pp. 3721–3725). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">https://doi.org/10.1109/ICASSP.2013.6638353</a>","mla":"Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3721–25, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>.","bibtex":"@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>}, 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} }","ama":"Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In: <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>","short":"M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 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 <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 3721–25, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">https://doi.org/10.1109/ICASSP.2013.6638353</a>."},"user_id":"44006","_id":"11816","page":"3721-3725","status":"public","department":[{"_id":"54"}],"type":"conference","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"],"date_created":"2019-07-12T05:28:48Z","abstract":[{"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.","lang":"eng"}],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf","relation":"supplementary_material","description":"Poster"}]},"publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","doi":"10.1109/ICASSP.2013.6638353","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf","open_access":"1"}],"date_updated":"2022-01-06T06:51:09Z","author":[{"last_name":"Hoang","first_name":"Manh Kha","full_name":"Hoang, Manh Kha"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"publication_identifier":{"issn":["1520-6149"]},"title":"Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning","year":"2013"},{"_id":"11938","page":"1577-1590","volume":17,"user_id":"44006","status":"public","oa":"1","citation":{"mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 17, no. 8, 2009, pp. 1577–90, doi:<a href=\"https://doi.org/10.1109/TASL.2009.2023172\">10.1109/TASL.2009.2023172</a>.","ama":"Windmann S, Haeb-Umbach R. Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2009;17(8):1577-1590. doi:<a href=\"https://doi.org/10.1109/TASL.2009.2023172\">10.1109/TASL.2009.2023172</a>","bibtex":"@article{Windmann_Haeb-Umbach_2009, title={Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition}, volume={17}, DOI={<a href=\"https://doi.org/10.1109/TASL.2009.2023172\">10.1109/TASL.2009.2023172</a>}, number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={1577–1590} }","apa":"Windmann, S., &#38; Haeb-Umbach, R. (2009). Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>17</i>(8), 1577–1590. <a href=\"https://doi.org/10.1109/TASL.2009.2023172\">https://doi.org/10.1109/TASL.2009.2023172</a>","ieee":"S. Windmann and R. Haeb-Umbach, “Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 17, no. 8, pp. 1577–1590, 2009.","chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 17, no. 8 (2009): 1577–90. <a href=\"https://doi.org/10.1109/TASL.2009.2023172\">https://doi.org/10.1109/TASL.2009.2023172</a>.","short":"S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 1577–1590."},"language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-2.pdf"}],"doi":"10.1109/TASL.2009.2023172","author":[{"full_name":"Windmann, Stefan","first_name":"Stefan","last_name":"Windmann"},{"id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"year":"2009","title":"Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition","intvolume":"        17","date_updated":"2022-01-06T06:51:12Z","date_created":"2019-07-12T05:31:09Z","department":[{"_id":"54"}],"type":"journal_article","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"],"issue":"8","publication":"IEEE Transactions on Audio, Speech, and Language Processing","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."}]}]