[{"page":"1-6","_id":"9879","language":[{"iso":"eng"}],"doi":"10.1109/ICPHM.2014.7036406","user_id":"55222","title":"PEM fuel cell prognostics using particle filter with model parameter adaptation","status":"public","year":"2014","author":[{"full_name":"Kimotho, James Kuria ","last_name":"Kimotho","first_name":"James Kuria "},{"last_name":"Meyer","first_name":"Tobias","full_name":"Meyer, Tobias"},{"full_name":"Sextro, Walter","last_name":"Sextro","first_name":"Walter","id":"21220"}],"date_updated":"2019-05-20T13:12:27Z","date_created":"2019-05-20T13:11:02Z","type":"conference","keyword":["ageing","particle filtering (numerical methods)","proton exchange membrane fuel cells","remaining life assessment","PEM fuel cell prognostics","PHM","RUL predictions","accelerated degradation","adaptive particle filter algorithm","dynamic loading","model parameter adaptation","prognostics and health management","proton exchange membrane fuel cells","remaining useful life estimation","self-healing effect","Adaptation models","Data models","Degradation","Estimation","Fuel cells","Mathematical model","Prognostics and health management"],"department":[{"_id":"151"}],"publication":"Prognostics and Health Management (PHM), 2014 IEEE Conference on","citation":{"apa":"Kimotho, J. K., Meyer, T., &#38; Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i> (pp. 1–6). <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>","ieee":"J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle filter with model parameter adaptation,” in <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i>, 2014, pp. 1–6.","short":"J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6.","chicago":"Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 1–6, 2014. <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>.","mla":"Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 2014, pp. 1–6, doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>.","ama":"Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter with model parameter adaptation. In: <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>. ; 2014:1-6. doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>","bibtex":"@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics using particle filter with model parameter adaptation}, DOI={<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>}, booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on}, author={Kimotho, James Kuria  and Meyer, Tobias and Sextro, Walter}, year={2014}, pages={1–6} }"},"abstract":[{"lang":"eng","text":"Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge."}]},{"abstract":[{"text":"Abstract In this paper we present an approach for synchronizing a wireless acoustic sensor network using a two-stage procedure. First the clock frequency and phase differences between pairs of nodes are estimated employing a two-way message exchange protocol. The estimates are further improved in a Kalman filter with a dedicated observation error model. In the second stage network-wide synchronization is achieved by means of a gossiping algorithm which estimates the average clock frequency and phase of the sensor nodes. These averages are viewed as frequency and phase of a virtual master clock, to which the clocks of the sensor nodes have to be adjusted. The amount of adjustment is computed in a specific control loop. While these steps are done in software, the actual sampling rate correction is carried out in hardware by using an adjustable frequency synthesizer. Experimental results obtained from hardware devices and software simulations of large scale networks are presented.","lang":"eng"}],"issue":"0","publication":"Signal Processing","department":[{"_id":"54"}],"keyword":["Gossip algorithm"],"type":"journal_article","date_created":"2019-07-12T05:30:23Z","date_updated":"2023-10-26T08:11:22Z","publication_identifier":{"issn":["0165-1684"]},"author":[{"first_name":"Joerg","last_name":"Schmalenstroeer","full_name":"Schmalenstroeer, Joerg","id":"460"},{"full_name":"Jebramcik, Patrick","first_name":"Patrick","last_name":"Jebramcik"},{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold","id":"242"}],"year":"2014","title":"A combined hardware-software approach for acoustic sensor network synchronization ","doi":"http://dx.doi.org/10.1016/j.sigpro.2014.06.030","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"http://www.sciencedirect.com/science/article/pii/S0165168414002990"}],"quality_controlled":"1","citation":{"apa":"Schmalenstroeer, J., Jebramcik, P., &#38; Haeb-Umbach, R. (2014). A combined hardware-software approach for acoustic sensor network synchronization . <i>Signal Processing</i>, <i>0</i>. <a href=\"http://dx.doi.org/10.1016/j.sigpro.2014.06.030\">http://dx.doi.org/10.1016/j.sigpro.2014.06.030</a>","ieee":"J. Schmalenstroeer, P. Jebramcik, and R. Haeb-Umbach, “A combined hardware-software approach for acoustic sensor network synchronization ,” <i>Signal Processing</i>, no. 0, p., 2014, doi: <a href=\"http://dx.doi.org/10.1016/j.sigpro.2014.06.030\">http://dx.doi.org/10.1016/j.sigpro.2014.06.030</a>.","chicago":"Schmalenstroeer, Joerg, Patrick Jebramcik, and Reinhold Haeb-Umbach. “A Combined Hardware-Software Approach for Acoustic Sensor Network Synchronization .” <i>Signal Processing</i>, no. 0 (2014). <a href=\"http://dx.doi.org/10.1016/j.sigpro.2014.06.030\">http://dx.doi.org/10.1016/j.sigpro.2014.06.030</a>.","short":"J. Schmalenstroeer, P. Jebramcik, R. Haeb-Umbach, Signal Processing (2014).","mla":"Schmalenstroeer, Joerg, et al. “A Combined Hardware-Software Approach for Acoustic Sensor Network Synchronization .” <i>Signal Processing</i>, no. 0, 2014, p., doi:<a href=\"http://dx.doi.org/10.1016/j.sigpro.2014.06.030\">http://dx.doi.org/10.1016/j.sigpro.2014.06.030</a>.","ama":"Schmalenstroeer J, Jebramcik P, Haeb-Umbach R. A combined hardware-software approach for acoustic sensor network synchronization . <i>Signal Processing</i>. 2014;(0). doi:<a href=\"http://dx.doi.org/10.1016/j.sigpro.2014.06.030\">http://dx.doi.org/10.1016/j.sigpro.2014.06.030</a>","bibtex":"@article{Schmalenstroeer_Jebramcik_Haeb-Umbach_2014, title={A combined hardware-software approach for acoustic sensor network synchronization }, DOI={<a href=\"http://dx.doi.org/10.1016/j.sigpro.2014.06.030\">http://dx.doi.org/10.1016/j.sigpro.2014.06.030</a>}, number={0}, journal={Signal Processing}, author={Schmalenstroeer, Joerg and Jebramcik, Patrick and Haeb-Umbach, Reinhold}, year={2014} }"},"oa":"1","status":"public","user_id":"460","_id":"11898","page":" - "},{"status":"public","user_id":"44006","page":"3721-3725","_id":"11816","citation":{"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>.","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>","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.","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>.","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.","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} }"},"oa":"1","date_updated":"2022-01-06T06:51:09Z","title":"Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning","year":"2013","publication_identifier":{"issn":["1520-6149"]},"author":[{"full_name":"Hoang, Manh Kha","last_name":"Hoang","first_name":"Manh Kha"},{"id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"doi":"10.1109/ICASSP.2013.6638353","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf"}],"language":[{"iso":"eng"}],"related_material":{"link":[{"relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf","description":"Poster"}]},"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"}],"publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","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"],"type":"conference","department":[{"_id":"54"}],"date_created":"2019-07-12T05:28:48Z"},{"year":"2013","title":"Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation","status":"public","publication_identifier":{"issn":["1520-6149"]},"author":[{"full_name":"Vu, Dang Hai Tran","first_name":"Dang Hai Tran","last_name":"Vu"},{"last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_updated":"2022-01-06T06:51:12Z","page":"863-867","language":[{"iso":"eng"}],"_id":"11917","user_id":"44006","doi":"10.1109/ICASSP.2013.6637771","publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","citation":{"bibtex":"@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013}, pages={863–867} }","chicago":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 863–67, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>.","short":"D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.","ama":"Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In: <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:863-867. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>","ieee":"D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation,” in <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–867.","apa":"Vu, D. H. T., &#38; Haeb-Umbach, R. (2013). Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i> (pp. 863–867). <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>","mla":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–67, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>."},"abstract":[{"lang":"eng","text":"In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm."}],"date_created":"2019-07-12T05:30:45Z","type":"conference","keyword":["correlation methods","estimation theory","hidden Markov models","iterative methods","probability","spectral analysis","speech processing","2D HMM","SPP estimates","iterative algorithm","posterior probability estimation","spectral correlation","speech presence probability estimation","state-of-the-art SPP estimation algorithm","temporal correlation","turbo principle","two-dimensional hidden Markov model","Correlation","Decoding","Estimation","Iterative decoding","Noise","Speech","Vectors"],"department":[{"_id":"54"}]},{"department":[{"_id":"34"},{"_id":"819"}],"type":"conference","keyword":["machine learning","exploratory landscape analysis","fitness landscape","benchmarking","evolutionary optimization","bbob test set","algorithm selection"],"date_created":"2023-08-04T15:51:56Z","abstract":[{"lang":"eng","text":"The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB’09/10 workshop."}],"publication":"Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation","doi":"10.1145/2330163.2330209","series_title":"GECCO ’12","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:48:48Z","publication_identifier":{"isbn":["9781450311779"]},"author":[{"full_name":"Bischl, Bernd","last_name":"Bischl","first_name":"Bernd"},{"first_name":"Olaf","last_name":"Mersmann","full_name":"Mersmann, Olaf"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Preuß, Mike","first_name":"Mike","last_name":"Preuß"}],"title":"Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning","year":"2012","place":"New York, NY, USA","citation":{"bibtex":"@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY, USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning}, DOI={<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>}, booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320}, collection={GECCO ’12} }","ama":"Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In: <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>. GECCO ’12. Association for Computing Machinery; 2012:313–320. doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>","mla":"Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, Association for Computing Machinery, 2012, pp. 313–320, doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>.","short":"B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2012, pp. 313–320.","chicago":"Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” In <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>.","ieee":"B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 2012, pp. 313–320, doi: <a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>.","apa":"Bischl, B., Mersmann, O., Trautmann, H., &#38; Preuß, M. (2012). Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>"},"user_id":"15504","_id":"46396","publisher":"Association for Computing Machinery","page":"313–320","status":"public"},{"date_updated":"2022-01-06T06:58:50Z","publication_status":"published","year":"2010","status":"public","title":"Clustering for Metric and Nonmetric Distance Measures","publication_identifier":{"issn":["1549-6325"]},"author":[{"full_name":"Ackermann, Marcel R.","first_name":"Marcel R.","last_name":"Ackermann"},{"id":"23","full_name":"Blömer, Johannes","last_name":"Blömer","first_name":"Johannes"},{"full_name":"Sohler, Christian","first_name":"Christian","last_name":"Sohler"}],"doi":"10.1145/1824777.1824779","user_id":"25078","page":"59:1--59:26","_id":"2990","publication":"ACM Trans. Algorithms","issue":"4","citation":{"apa":"Ackermann, M. R., Blömer, J., &#38; Sohler, C. (2010). Clustering for Metric and Nonmetric Distance Measures. <i>ACM Trans. Algorithms</i>, (4), 59:1--59:26. <a href=\"https://doi.org/10.1145/1824777.1824779\">https://doi.org/10.1145/1824777.1824779</a>","ieee":"M. R. Ackermann, J. Blömer, and C. Sohler, “Clustering for Metric and Nonmetric Distance Measures,” <i>ACM Trans. Algorithms</i>, no. 4, pp. 59:1--59:26, 2010.","short":"M.R. Ackermann, J. Blömer, C. Sohler, ACM Trans. Algorithms (2010) 59:1--59:26.","chicago":"Ackermann, Marcel R., Johannes Blömer, and Christian Sohler. “Clustering for Metric and Nonmetric Distance Measures.” <i>ACM Trans. Algorithms</i>, no. 4 (2010): 59:1--59:26. <a href=\"https://doi.org/10.1145/1824777.1824779\">https://doi.org/10.1145/1824777.1824779</a>.","mla":"Ackermann, Marcel R., et al. “Clustering for Metric and Nonmetric Distance Measures.” <i>ACM Trans. Algorithms</i>, no. 4, 2010, pp. 59:1--59:26, doi:<a href=\"https://doi.org/10.1145/1824777.1824779\">10.1145/1824777.1824779</a>.","ama":"Ackermann MR, Blömer J, Sohler C. Clustering for Metric and Nonmetric Distance Measures. <i>ACM Trans Algorithms</i>. 2010;(4):59:1--59:26. doi:<a href=\"https://doi.org/10.1145/1824777.1824779\">10.1145/1824777.1824779</a>","bibtex":"@article{Ackermann_Blömer_Sohler_2010, title={Clustering for Metric and Nonmetric Distance Measures}, DOI={<a href=\"https://doi.org/10.1145/1824777.1824779\">10.1145/1824777.1824779</a>}, number={4}, journal={ACM Trans. Algorithms}, author={Ackermann, Marcel R. and Blömer, Johannes and Sohler, Christian}, year={2010}, pages={59:1--59:26} }"},"type":"journal_article","keyword":["k-means clustering","k-median clustering","Approximation algorithm","Bregman divergences","Itakura-Saito divergence","Kullback-Leibler divergence","Mahalanobis distance","random sampling"],"department":[{"_id":"64"}],"date_created":"2018-06-05T07:52:41Z"},{"language":[{"iso":"eng"}],"_id":"11913","page":"241-244","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2010/DaHa10-2.pdf"}],"doi":"10.1109/ICASSP.2010.5495994","user_id":"44006","author":[{"full_name":"Tran Vu, Dang Hai","last_name":"Tran Vu","first_name":"Dang Hai"},{"id":"242","first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold"}],"status":"public","title":"Blind speech separation employing directional statistics in an Expectation Maximization framework","year":"2010","date_updated":"2022-01-06T06:51:12Z","date_created":"2019-07-12T05:30:40Z","oa":"1","department":[{"_id":"54"}],"keyword":["array signal processing","blind source separation","blind speech separation","complex vector space","complex Watson distribution","directional statistics","expectation-maximisation algorithm","expectation maximization algorithm","Fourier transform","Fourier transforms","generalized sidelobe canceller","interference suppression","maximum signal-to-noise ratio beamformer","microphone signal","probabilistic model","spatial aliasing","spatial beamforming configuration","speech enhancement","statistical distributions"],"type":"conference","citation":{"ieee":"D. H. Tran Vu and R. Haeb-Umbach, “Blind speech separation employing directional statistics in an Expectation Maximization framework,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>, 2010, pp. 241–244.","apa":"Tran Vu, D. H., &#38; Haeb-Umbach, R. (2010). Blind speech separation employing directional statistics in an Expectation Maximization framework. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i> (pp. 241–244). <a href=\"https://doi.org/10.1109/ICASSP.2010.5495994\">https://doi.org/10.1109/ICASSP.2010.5495994</a>","chicago":"Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing Directional Statistics in an Expectation Maximization Framework.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>, 241–44, 2010. <a href=\"https://doi.org/10.1109/ICASSP.2010.5495994\">https://doi.org/10.1109/ICASSP.2010.5495994</a>.","short":"D.H. Tran Vu, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 2010, pp. 241–244.","mla":"Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing Directional Statistics in an Expectation Maximization Framework.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>, 2010, pp. 241–44, doi:<a href=\"https://doi.org/10.1109/ICASSP.2010.5495994\">10.1109/ICASSP.2010.5495994</a>.","bibtex":"@inproceedings{Tran Vu_Haeb-Umbach_2010, title={Blind speech separation employing directional statistics in an Expectation Maximization framework}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2010.5495994\">10.1109/ICASSP.2010.5495994</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)}, author={Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2010}, pages={241–244} }","ama":"Tran Vu DH, Haeb-Umbach R. Blind speech separation employing directional statistics in an Expectation Maximization framework. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>. ; 2010:241-244. doi:<a href=\"https://doi.org/10.1109/ICASSP.2010.5495994\">10.1109/ICASSP.2010.5495994</a>"},"publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)","abstract":[{"lang":"eng","text":"In this paper we propose to employ directional statistics in a complex vector space to approach the problem of blind speech separation in the presence of spatially correlated noise. We interpret the values of the short time Fourier transform of the microphone signals to be draws from a mixture of complex Watson distributions, a probabilistic model which naturally accounts for spatial aliasing. The parameters of the density are related to the a priori source probabilities, the power of the sources and the transfer function ratios from sources to sensors. Estimation formulas are derived for these parameters by employing the Expectation Maximization (EM) algorithm. The E-step corresponds to the estimation of the source presence probabilities for each time-frequency bin, while the M-step leads to a maximum signal-to-noise ratio (MaxSNR) beamformer in the presence of uncertainty about the source activity. Experimental results are reported for an implementation in a generalized sidelobe canceller (GSC) like spatial beamforming configuration for 3 speech sources with significant coherent noise in reverberant environments, demonstrating the usefulness of the novel modeling framework."}]},{"department":[{"_id":"54"}],"oa":"1","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"],"type":"conference","date_created":"2019-07-12T05:27:01Z","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."}],"citation":{"apa":"Bevermeier, M., Peschke, S., &#38; Haeb-Umbach, R. (2009). Robust vehicle localization based on multi-level sensor fusion and online parameter estimation. In <i>6th Workshop on Positioning Navigation and Communication (WPNC 2009)</i> (pp. 235–242). <a href=\"https://doi.org/10.1109/WPNC.2009.4907833\">https://doi.org/10.1109/WPNC.2009.4907833</a>","ieee":"M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Robust vehicle localization based on multi-level sensor fusion and online parameter estimation,” in <i>6th Workshop on Positioning Navigation and Communication (WPNC 2009)</i>, 2009, pp. 235–242.","short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 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 <i>6th Workshop on Positioning Navigation and Communication (WPNC 2009)</i>, 235–42, 2009. <a href=\"https://doi.org/10.1109/WPNC.2009.4907833\">https://doi.org/10.1109/WPNC.2009.4907833</a>.","mla":"Bevermeier, Maik, et al. “Robust Vehicle Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.” <i>6th Workshop on Positioning Navigation and Communication (WPNC 2009)</i>, 2009, pp. 235–42, doi:<a href=\"https://doi.org/10.1109/WPNC.2009.4907833\">10.1109/WPNC.2009.4907833</a>.","ama":"Bevermeier M, Peschke S, Haeb-Umbach R. Robust vehicle localization based on multi-level sensor fusion and online parameter estimation. In: <i>6th Workshop on Positioning Navigation and Communication (WPNC 2009)</i>. ; 2009:235-242. doi:<a href=\"https://doi.org/10.1109/WPNC.2009.4907833\">10.1109/WPNC.2009.4907833</a>","bibtex":"@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}, DOI={<a href=\"https://doi.org/10.1109/WPNC.2009.4907833\">10.1109/WPNC.2009.4907833</a>}, 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} }"},"publication":"6th Workshop on Positioning Navigation and Communication (WPNC 2009)","user_id":"44006","doi":"10.1109/WPNC.2009.4907833","_id":"11723","language":[{"iso":"eng"}],"page":"235-242","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf","open_access":"1"}],"date_updated":"2022-01-06T06:51:07Z","author":[{"first_name":"Maik","last_name":"Bevermeier","full_name":"Bevermeier, Maik"},{"first_name":"Sven","last_name":"Peschke","full_name":"Peschke, Sven"},{"id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"year":"2009","title":"Robust vehicle localization based on multi-level sensor fusion and online parameter estimation","status":"public"},{"date_updated":"2022-01-06T06:51:07Z","status":"public","title":"Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning","year":"2009","author":[{"full_name":"Bevermeier, Maik","first_name":"Maik","last_name":"Bevermeier"},{"first_name":"Sven","last_name":"Peschke","full_name":"Peschke, Sven"},{"id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"doi":"10.1109/VETECS.2009.5073634","user_id":"44006","page":"1-5","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf","open_access":"1"}],"_id":"11724","language":[{"iso":"eng"}],"abstract":[{"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.","lang":"eng"}],"publication":"IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)","citation":{"chicago":"Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.” In <i>IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)</i>, 1–5, 2009. <a href=\"https://doi.org/10.1109/VETECS.2009.5073634\">https://doi.org/10.1109/VETECS.2009.5073634</a>.","short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 2009, pp. 1–5.","apa":"Bevermeier, M., Peschke, S., &#38; Haeb-Umbach, R. (2009). Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning. In <i>IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)</i> (pp. 1–5). <a href=\"https://doi.org/10.1109/VETECS.2009.5073634\">https://doi.org/10.1109/VETECS.2009.5073634</a>","ieee":"M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning,” in <i>IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)</i>, 2009, pp. 1–5.","ama":"Bevermeier M, Peschke S, Haeb-Umbach R. Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning. In: <i>IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)</i>. ; 2009:1-5. doi:<a href=\"https://doi.org/10.1109/VETECS.2009.5073634\">10.1109/VETECS.2009.5073634</a>","bibtex":"@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning}, DOI={<a href=\"https://doi.org/10.1109/VETECS.2009.5073634\">10.1109/VETECS.2009.5073634</a>}, booktitle={IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}, author={Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009}, pages={1–5} }","mla":"Bevermeier, Maik, et al. “Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.” <i>IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)</i>, 2009, pp. 1–5, doi:<a href=\"https://doi.org/10.1109/VETECS.2009.5073634\">10.1109/VETECS.2009.5073634</a>."},"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"],"type":"conference","oa":"1","department":[{"_id":"54"}],"date_created":"2019-07-12T05:27:02Z"},{"citation":{"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} }","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>","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>.","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.","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.","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>"},"oa":"1","status":"public","_id":"11938","page":"1577-1590","volume":17,"user_id":"44006","publication":"IEEE Transactions on Audio, Speech, and Language Processing","issue":"8","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."}],"date_created":"2019-07-12T05:31:09Z","department":[{"_id":"54"}],"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"],"type":"journal_article","author":[{"full_name":"Windmann, Stefan","last_name":"Windmann","first_name":"Stefan"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold","last_name":"Haeb-Umbach"}],"title":"Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition","year":"2009","intvolume":"        17","date_updated":"2022-01-06T06:51:12Z","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"},{"issue":"5","publication":"IEEE Transactions on Audio, Speech, and Language Processing","abstract":[{"text":"Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the presence of spatially colored noise leads to a generalized eigenvalue problem. While this approach has extensively been employed in narrowband (antenna) array beamforming, it is typically not used for broadband (microphone) array beamforming due to the uncontrolled amount of speech distortion introduced by a narrowband SNR criterion. In this paper, we show how the distortion of the desired signal can be controlled by a single-channel post-filter, resulting in a performance comparable to the generalized minimum variance distortionless response beamformer, where arbitrary transfer functions relate the source and the microphones. Results are given both for directional and diffuse noise. A novel gradient ascent adaptation algorithm is presented, and its good convergence properties are experimentally revealed by comparison with alternatives from the literature. A key feature of the proposed beamformer is that it operates blindly, i.e., it neither requires knowledge about the array geometry nor an explicit estimation of the transfer functions from source to sensors or the direction-of-arrival.","lang":"eng"}],"date_created":"2019-07-12T05:30:57Z","keyword":["acoustic signal processing","arbitrary transfer function","array signal processing","blind acoustic beamforming","direction-of-arrival","direction-of-arrival estimation","eigenvalues and eigenfunctions","generalized eigenvalue decomposition","gradient ascent adaptation algorithm","microphone arrays","microphones","narrowband array beamforming","sensor array","single-channel post-filter","spatially colored noise","transfer functions"],"type":"journal_article","department":[{"_id":"54"}],"year":"2007","title":"Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition","author":[{"first_name":"Ernst","last_name":"Warsitz","full_name":"Warsitz, Ernst"},{"full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242"}],"date_updated":"2022-01-06T06:51:12Z","intvolume":"        15","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2007/WaHa07.pdf","open_access":"1"}],"language":[{"iso":"eng"}],"doi":"10.1109/TASL.2007.898454","citation":{"bibtex":"@article{Warsitz_Haeb-Umbach_2007, title={Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition}, volume={15}, DOI={<a href=\"https://doi.org/10.1109/TASL.2007.898454\">10.1109/TASL.2007.898454</a>}, number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2007}, pages={1529–1539} }","ama":"Warsitz E, Haeb-Umbach R. Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2007;15(5):1529-1539. doi:<a href=\"https://doi.org/10.1109/TASL.2007.898454\">10.1109/TASL.2007.898454</a>","mla":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 15, no. 5, 2007, pp. 1529–39, doi:<a href=\"https://doi.org/10.1109/TASL.2007.898454\">10.1109/TASL.2007.898454</a>.","short":"E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 15 (2007) 1529–1539.","chicago":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 15, no. 5 (2007): 1529–39. <a href=\"https://doi.org/10.1109/TASL.2007.898454\">https://doi.org/10.1109/TASL.2007.898454</a>.","ieee":"E. Warsitz and R. Haeb-Umbach, “Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 15, no. 5, pp. 1529–1539, 2007.","apa":"Warsitz, E., &#38; Haeb-Umbach, R. (2007). Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>15</i>(5), 1529–1539. <a href=\"https://doi.org/10.1109/TASL.2007.898454\">https://doi.org/10.1109/TASL.2007.898454</a>"},"oa":"1","status":"public","page":"1529-1539","_id":"11927","user_id":"44006","volume":15},{"status":"public","_id":"11943","page":"I","volume":1,"user_id":"44006","citation":{"ama":"Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I. doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>","bibtex":"@inproceedings{Windmann_Haeb-Umbach_2006, title={Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters}, volume={1}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006}, pages={I} }","mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol. 1, 2006, p. I, doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>.","chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 1:I, 2006. <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>.","short":"S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.","apa":"Windmann, S., &#38; Haeb-Umbach, R. (2006). Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol. 1, p. I). <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>","ieee":"S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006, vol. 1, p. I."},"oa":"1","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"}],"title":"Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters","year":"2006","intvolume":"         1","date_updated":"2022-01-06T06:51:12Z","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf"}],"doi":"10.1109/ICASSP.2006.1660058","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)","abstract":[{"text":"A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved","lang":"eng"}],"date_created":"2019-07-12T05:31:15Z","department":[{"_id":"54"}],"keyword":["clean speech training data","iterative methods","iterative speech enhancement","Kalman filter","Kalman filters","Kalman-LM-iterative algorithm","line spectral pair parameters","log-spectral distance","marginalized particle filter","noise level","nonlinear dynamic state speech model","particle filtering (numerical methods)","single channel speech enhancement","SNR gains","speech enhancement","speech samples"],"type":"conference"},{"date_created":"2019-07-12T05:31:00Z","department":[{"_id":"54"}],"keyword":["acoustic filter-and-sum beamforming","acoustic room impulses","acoustic signal processing","adaptive principal component analysis","adaptive signal processing","architectural acoustics","constrained optimization problem","cross power spectral density","deterministic algorithm","deterministic algorithms","distant-talking environments","eigenvalues and eigenfunctions","eigenvector","enhanced signal","filter-and-sum beamformer","FIR filter coefficients","FIR filter coefficients","FIR filters","gradient methods","human-machine interfaces","iterative estimation","iterative methods","largest eigenvalue","microphone signals","multichannel signal processing","optimisation","principal component analysis","spectral analysis","stochastic gradient ascent algorithm","stochastic processes"],"type":"conference","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)","abstract":[{"lang":"eng","text":"For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm."}],"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2005/WaHa05.pdf","open_access":"1"}],"doi":"10.1109/ICASSP.2005.1416129","author":[{"full_name":"Warsitz, Ernst","first_name":"Ernst","last_name":"Warsitz"},{"id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"title":"Acoustic filter-and-sum beamforming by adaptive principal component analysis","year":"2005","intvolume":"         4","date_updated":"2022-01-06T06:51:12Z","oa":"1","citation":{"ama":"Warsitz E, Haeb-Umbach R. Acoustic filter-and-sum beamforming by adaptive principal component analysis. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>. Vol 4. ; 2005:iv/797-iv/800 Vol. 4. doi:<a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">10.1109/ICASSP.2005.1416129</a>","bibtex":"@inproceedings{Warsitz_Haeb-Umbach_2005, title={Acoustic filter-and-sum beamforming by adaptive principal component analysis}, volume={4}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">10.1109/ICASSP.2005.1416129</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2005}, pages={iv/797-iv/800 Vol. 4} }","mla":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming by Adaptive Principal Component Analysis.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, vol. 4, 2005, p. iv/797-iv/800 Vol. 4, doi:<a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">10.1109/ICASSP.2005.1416129</a>.","short":"E. Warsitz, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005), 2005, p. iv/797-iv/800 Vol. 4.","chicago":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming by Adaptive Principal Component Analysis.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, 4:iv/797-iv/800 Vol. 4, 2005. <a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">https://doi.org/10.1109/ICASSP.2005.1416129</a>.","apa":"Warsitz, E., &#38; Haeb-Umbach, R. (2005). Acoustic filter-and-sum beamforming by adaptive principal component analysis. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i> (Vol. 4, p. iv/797-iv/800 Vol. 4). <a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">https://doi.org/10.1109/ICASSP.2005.1416129</a>","ieee":"E. Warsitz and R. Haeb-Umbach, “Acoustic filter-and-sum beamforming by adaptive principal component analysis,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, 2005, vol. 4, p. iv/797-iv/800 Vol. 4."},"_id":"11930","page":"iv/797-iv/800 Vol. 4","volume":4,"user_id":"44006","status":"public"},{"date_updated":"2022-01-06T06:51:12Z","author":[{"full_name":"Warsitz, Ernst","first_name":"Ernst","last_name":"Warsitz"},{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold","id":"242"}],"year":"2004","title":"Robust speaker direction estimation with particle filtering","status":"public","user_id":"44006","doi":"10.1109/MMSP.2004.1436569","language":[{"iso":"eng"}],"_id":"11931","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2004/WaHa04.pdf","open_access":"1"}],"page":"367-370","abstract":[{"lang":"eng","text":"The paper is concerned with binaural signal processing for a bimodal human-robot interface with hearing and vision. The two microphone signals are processed to obtain an enhanced single-channel input signal for the subsequent speech recognizer and to localize the acoustic source, an important information for establishing a natural human-robot communication. We utilize a robust adaptive algorithm for filter-and-sum beamforming (FSB) and extract speaker direction information from the resulting FIR filter coefficients. Further, particle filtering is applied which conducts a nonlinear Bayesian tracking of speaker movement. Good location accuracy can be achieved even in highly reverberant environments. The results obtained outperform the conventional generalized cross correlation (GCC) method."}],"citation":{"ama":"Warsitz E, Haeb-Umbach R. Robust speaker direction estimation with particle filtering. In: <i>IEEE Workshop on Multimedia Signal Processing (MMSP 2004)</i>. ; 2004:367-370. doi:<a href=\"https://doi.org/10.1109/MMSP.2004.1436569\">10.1109/MMSP.2004.1436569</a>","bibtex":"@inproceedings{Warsitz_Haeb-Umbach_2004, title={Robust speaker direction estimation with particle filtering}, DOI={<a href=\"https://doi.org/10.1109/MMSP.2004.1436569\">10.1109/MMSP.2004.1436569</a>}, booktitle={IEEE Workshop on Multimedia Signal Processing (MMSP 2004)}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2004}, pages={367–370} }","mla":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation with Particle Filtering.” <i>IEEE Workshop on Multimedia Signal Processing (MMSP 2004)</i>, 2004, pp. 367–70, doi:<a href=\"https://doi.org/10.1109/MMSP.2004.1436569\">10.1109/MMSP.2004.1436569</a>.","chicago":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation with Particle Filtering.” In <i>IEEE Workshop on Multimedia Signal Processing (MMSP 2004)</i>, 367–70, 2004. <a href=\"https://doi.org/10.1109/MMSP.2004.1436569\">https://doi.org/10.1109/MMSP.2004.1436569</a>.","short":"E. Warsitz, R. Haeb-Umbach, in: IEEE Workshop on Multimedia Signal Processing (MMSP 2004), 2004, pp. 367–370.","apa":"Warsitz, E., &#38; Haeb-Umbach, R. (2004). Robust speaker direction estimation with particle filtering. In <i>IEEE Workshop on Multimedia Signal Processing (MMSP 2004)</i> (pp. 367–370). <a href=\"https://doi.org/10.1109/MMSP.2004.1436569\">https://doi.org/10.1109/MMSP.2004.1436569</a>","ieee":"E. Warsitz and R. Haeb-Umbach, “Robust speaker direction estimation with particle filtering,” in <i>IEEE Workshop on Multimedia Signal Processing (MMSP 2004)</i>, 2004, pp. 367–370."},"publication":"IEEE Workshop on Multimedia Signal Processing (MMSP 2004)","department":[{"_id":"54"}],"oa":"1","type":"conference","keyword":["bimodal human-robot interface","binaural signal processing","enhanced single-channel input signal","filter-and-sum beamforming","filtering theory","FIR filter coefficient","generalized cross correlation method","microphones","microphone signal","nonlinear Bayesian tracking","particle filtering","robust adaptive algorithm","robust speaker direction estimation","signal processing","speech enhancement","speech recognition","speech recognizer","user interfaces"],"date_created":"2019-07-12T05:31:01Z"}]