[{"publication_status":"published","publication_identifier":{"issn":["0888-3270"]},"citation":{"ama":"Itner D, Dreiling D, Gravenkamp H, Henning B, Birk C. A modified Levenberg–Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses. <i>Mechanical Systems and Signal Processing</i>. 2026;247:113904. doi:<a href=\"https://doi.org/10.1016/j.ymssp.2026.113904\">https://doi.org/10.1016/j.ymssp.2026.113904</a>","chicago":"Itner, Dominik, Dmitrij Dreiling, Hauke Gravenkamp, Bernd Henning, and Carolin Birk. “A Modified Levenberg–Marquardt Method for Estimating the Elastic Material Parameters of Polymer Waveguides Using Residuals between Autocorrelated Frequency Responses.” <i>Mechanical Systems and Signal Processing</i> 247 (2026): 113904. <a href=\"https://doi.org/10.1016/j.ymssp.2026.113904\">https://doi.org/10.1016/j.ymssp.2026.113904</a>.","ieee":"D. Itner, D. Dreiling, H. Gravenkamp, B. Henning, and C. Birk, “A modified Levenberg–Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses,” <i>Mechanical Systems and Signal Processing</i>, vol. 247, p. 113904, 2026, doi: <a href=\"https://doi.org/10.1016/j.ymssp.2026.113904\">https://doi.org/10.1016/j.ymssp.2026.113904</a>.","apa":"Itner, D., Dreiling, D., Gravenkamp, H., Henning, B., &#38; Birk, C. (2026). A modified Levenberg–Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses. <i>Mechanical Systems and Signal Processing</i>, <i>247</i>, 113904. <a href=\"https://doi.org/10.1016/j.ymssp.2026.113904\">https://doi.org/10.1016/j.ymssp.2026.113904</a>","mla":"Itner, Dominik, et al. “A Modified Levenberg–Marquardt Method for Estimating the Elastic Material Parameters of Polymer Waveguides Using Residuals between Autocorrelated Frequency Responses.” <i>Mechanical Systems and Signal Processing</i>, vol. 247, 2026, p. 113904, doi:<a href=\"https://doi.org/10.1016/j.ymssp.2026.113904\">https://doi.org/10.1016/j.ymssp.2026.113904</a>.","short":"D. Itner, D. Dreiling, H. Gravenkamp, B. Henning, C. Birk, Mechanical Systems and Signal Processing 247 (2026) 113904.","bibtex":"@article{Itner_Dreiling_Gravenkamp_Henning_Birk_2026, title={A modified Levenberg–Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses}, volume={247}, DOI={<a href=\"https://doi.org/10.1016/j.ymssp.2026.113904\">https://doi.org/10.1016/j.ymssp.2026.113904</a>}, journal={Mechanical Systems and Signal Processing}, author={Itner, Dominik and Dreiling, Dmitrij and Gravenkamp, Hauke and Henning, Bernd and Birk, Carolin}, year={2026}, pages={113904} }"},"intvolume":"       247","page":"113904","author":[{"full_name":"Itner, Dominik","last_name":"Itner","first_name":"Dominik"},{"first_name":"Dmitrij","full_name":"Dreiling, Dmitrij","id":"32616","last_name":"Dreiling"},{"first_name":"Hauke","last_name":"Gravenkamp","full_name":"Gravenkamp, Hauke"},{"id":"213","full_name":"Henning, Bernd","last_name":"Henning","first_name":"Bernd"},{"last_name":"Birk","full_name":"Birk, Carolin","first_name":"Carolin"}],"volume":247,"date_updated":"2026-02-02T12:44:47Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://www.sciencedirect.com/science/article/pii/S0888327026000610/pdfft?md5=16e8493b44527f4ab0a6d13f634a01c3&pid=1-s2.0-S0888327026000610-main.pdf"}],"doi":"https://doi.org/10.1016/j.ymssp.2026.113904","type":"journal_article","status":"public","user_id":"32616","department":[{"_id":"49"}],"project":[{"name":"Vollständige Bestimmung der akustischen Materialparameter von Polymeren","_id":"89"}],"_id":"63800","year":"2026","date_created":"2026-01-29T08:53:42Z","title":"A modified Levenberg–Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses","publication":"Mechanical Systems and Signal Processing","abstract":[{"lang":"eng","text":"In this contribution, we address the estimation of the frequency-dependent elastic parameters of polymers in the ultrasound range, which is formulated as an inverse problem. This inverse problem is implemented as a nonlinear regression-type optimization problem, in which the simulation signals are fitted to the measurement signals. These signals consist of displacement responses in waveguides, focusing on hollow cylindrical geometries to enhance the simulation efficiency. To accelerate the optimization and reduce the number of model evaluations and wait times, we propose two novel methods. First, we introduce an adaptation of the Levenberg–Marquardt method derived from a geometrical interpretation of the least-squares optimization problem. Second, we introduce an improved objective function based on the autocorrelated envelopes of the measurement and simulation signals. Given that this study primarily relies on simulation data to quantify optimization convergence, we aggregate the expected ranges of realistic material parameters and derive their distributions to ensure the reproducibility of optimizations with proper measurements. We demonstrate the effectiveness of our objective function modification and step adaptation for various materials with isotropic material symmetry by comparing them with the Broyden–Fletcher–Goldfarb–Shanno method. In all cases, our method reduces the total number of model evaluations, thereby shortening the time to identify the material parameters."}],"language":[{"iso":"eng"}],"keyword":["Material parameter estimation","Waveguide","Nonlinear optimization","Inverse problem","Least squares"]},{"date_updated":"2025-05-27T09:10:09Z","date_created":"2025-05-02T09:22:39Z","author":[{"first_name":"Tobias Martin","id":"92810","full_name":"Peters, Tobias Martin","last_name":"Peters","orcid":"0009-0008-5193-6243"},{"id":"451","full_name":"Scharlau, Ingrid","orcid":"0000-0003-2364-9489","last_name":"Scharlau","first_name":"Ingrid"}],"volume":16,"title":"Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?","doi":"10.3389/fpsyg.2025.1574809","publication_status":"published","year":"2025","citation":{"apa":"Peters, T. M., &#38; Scharlau, I. (2025). Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications? <i>Frontiers in Psychology</i>, <i>16</i>. <a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">https://doi.org/10.3389/fpsyg.2025.1574809</a>","mla":"Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI: Is Distrust Helpful When Receiving AI Misclassifications?” <i>Frontiers in Psychology</i>, vol. 16, 2025, doi:<a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>.","short":"T.M. Peters, I. Scharlau, Frontiers in Psychology 16 (2025).","bibtex":"@article{Peters_Scharlau_2025, title={Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}, volume={16}, DOI={<a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>}, journal={Frontiers in Psychology}, author={Peters, Tobias Martin and Scharlau, Ingrid}, year={2025} }","ama":"Peters TM, Scharlau I. Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications? <i>Frontiers in Psychology</i>. 2025;16. doi:<a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>","ieee":"T. M. Peters and I. Scharlau, “Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?,” <i>Frontiers in Psychology</i>, vol. 16, 2025, doi: <a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>.","chicago":"Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI: Is Distrust Helpful When Receiving AI Misclassifications?” <i>Frontiers in Psychology</i> 16 (2025). <a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">https://doi.org/10.3389/fpsyg.2025.1574809</a>."},"intvolume":"        16","project":[{"name":"TRR 318 - C1: TRR 318 - Subproject C1 - Gesundes Misstrauen in Erklärungen","_id":"124"}],"_id":"59755","user_id":"92810","department":[{"_id":"424"},{"_id":"660"}],"article_type":"original","keyword":["trust in AI","trust","distrust","human-AI interaction","Signal Detection Theory","Bayesian parameter estimation","image classification"],"language":[{"iso":"eng"}],"type":"journal_article","publication":"Frontiers in Psychology","abstract":[{"text":"Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine,\r\ntrustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception,\r\nfor example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated\r\nclassification as advice. Research to improve such interactions often aims to foster the\r\nuser’s trust, which in turn should improve the combined human-AI performance. Given that AI\r\nmodels can err, we argue that the possibility to critically review, thus to distrust, an AI decision is\r\nan equally interesting target of research.\r\nWe created two image classification scenarios in which the participants received mock-up\r\nAI advice. The quality of the advice decreases for a phase of the experiment. We studied the\r\ntask performance, trust and distrust of the participants, and tested whether an instruction to\r\nremain skeptical and review each piece of advice led to a better performance compared to a\r\nneutral condition. Our results indicate that this instruction does not improve but rather worsens\r\nthe participants’ performance. Repeated single-item self-report of trust and distrust shows an\r\nincrease in trust and a decrease in distrust after the drop in the AI’s classification quality, with no\r\ndifference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory\r\nanalysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether\r\nthe problems of under- and over-reliance have been mitigated. We discuss implications of our\r\nresults for the usage of disclaimers before interacting with AI, as prominently used in current\r\nLLM-based chatbots, and for trust and distrust research.","lang":"eng"}],"status":"public"},{"year":"2013","page":"3352-3356","citation":{"mla":"Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.” <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3352–56, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">10.1109/ICASSP.2013.6638279</a>.","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={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">10.1109/ICASSP.2013.6638279</a>}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013}, pages={3352–3356} }","short":"A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.","apa":"Chinaev, A., &#38; Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i> (pp. 3352–3356). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">https://doi.org/10.1109/ICASSP.2013.6638279</a>","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 <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 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 <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 3352–56, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">https://doi.org/10.1109/ICASSP.2013.6638279</a>.","ama":"Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In: <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:3352-3356. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">10.1109/ICASSP.2013.6638279</a>"},"publication_identifier":{"issn":["1520-6149"]},"related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf"}]},"title":"MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations","doi":"10.1109/ICASSP.2013.6638279","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf"}],"date_updated":"2022-01-06T06:51:08Z","oa":"1","date_created":"2019-07-12T05:27:20Z","author":[{"full_name":"Chinaev, Aleksej","last_name":"Chinaev","first_name":"Aleksej"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"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."}],"status":"public","publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","type":"conference","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"}],"_id":"11740","department":[{"_id":"54"}],"user_id":"44006"},{"author":[{"last_name":"Hoang","full_name":"Hoang, Manh Kha","first_name":"Manh Kha"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"date_created":"2019-07-12T05:28:48Z","date_updated":"2022-01-06T06:51:09Z","oa":"1","doi":"10.1109/ICASSP.2013.6638353","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf"}],"title":"Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning","related_material":{"link":[{"description":"Poster","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf"}]},"publication_identifier":{"issn":["1520-6149"]},"page":"3721-3725","citation":{"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>","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>.","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>","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} }","short":"M.K. Hoang, R. Haeb-Umbach, 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.” <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>."},"year":"2013","department":[{"_id":"54"}],"user_id":"44006","_id":"11816","language":[{"iso":"eng"}],"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"],"publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","type":"conference","status":"public","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."}]},{"title":"Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12.pdf"}],"oa":"1","date_updated":"2022-01-06T06:51:08Z","author":[{"full_name":"Chinaev, Aleksej","last_name":"Chinaev","first_name":"Aleksej"},{"full_name":"Krueger, Alexander","last_name":"Krueger","first_name":"Alexander"},{"first_name":"Dang Hai","last_name":"Tran Vu","full_name":"Tran Vu, Dang Hai"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"date_created":"2019-07-12T05:27:26Z","year":"2012","citation":{"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 <i>37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>, 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 <i>37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>, 2012.","ama":"Chinaev A, Krueger A, Tran Vu DH, Haeb-Umbach R. Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In: <i>37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>. ; 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.","mla":"Chinaev, Aleksej, et al. “Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.” <i>37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>, 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} }","apa":"Chinaev, A., Krueger, A., Tran Vu, D. H., &#38; Haeb-Umbach, R. (2012). Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In <i>37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>."},"related_material":{"link":[{"relation":"supplementary_material","description":"Presentation","url":"https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12_Talk.pdf"}]},"keyword":["MAP parameter estimation","noise power estimation","speech enhancement"],"language":[{"iso":"eng"}],"_id":"11745","department":[{"_id":"54"}],"user_id":"44006","abstract":[{"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.","lang":"eng"}],"status":"public","publication":"37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)","type":"conference"},{"year":"2009","page":"235-242","citation":{"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>.","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} }","short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–242.","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>","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>.","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.","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>"},"date_updated":"2022-01-06T06:51:07Z","oa":"1","date_created":"2019-07-12T05:27:01Z","author":[{"last_name":"Bevermeier","full_name":"Bevermeier, Maik","first_name":"Maik"},{"first_name":"Sven","full_name":"Peschke, Sven","last_name":"Peschke"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"title":"Robust vehicle localization based on multi-level sensor fusion and online parameter estimation","doi":"10.1109/WPNC.2009.4907833","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf"}],"publication":"6th Workshop on Positioning Navigation and Communication (WPNC 2009)","type":"conference","abstract":[{"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.","lang":"eng"}],"status":"public","_id":"11723","department":[{"_id":"54"}],"user_id":"44006","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"}]},{"_id":"11724","department":[{"_id":"54"}],"user_id":"44006","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"}],"publication":"IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)","type":"conference","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."}],"status":"public","date_updated":"2022-01-06T06:51:07Z","oa":"1","author":[{"first_name":"Maik","last_name":"Bevermeier","full_name":"Bevermeier, Maik"},{"first_name":"Sven","full_name":"Peschke, Sven","last_name":"Peschke"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"date_created":"2019-07-12T05:27:02Z","title":"Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning","doi":"10.1109/VETECS.2009.5073634","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf"}],"year":"2009","page":"1-5","citation":{"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>","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>.","short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 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>","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>.","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."}},{"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"}],"_id":"11938","department":[{"_id":"54"}],"user_id":"44006","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."}],"status":"public","publication":"IEEE Transactions on Audio, Speech, and Language Processing","type":"journal_article","title":"Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition","doi":"10.1109/TASL.2009.2023172","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-2.pdf"}],"date_updated":"2022-01-06T06:51:12Z","oa":"1","volume":17,"author":[{"first_name":"Stefan","full_name":"Windmann, Stefan","last_name":"Windmann"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_created":"2019-07-12T05:31:09Z","year":"2009","intvolume":"        17","page":"1577-1590","citation":{"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>","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>.","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>","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} }","short":"S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 1577–1590.","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>."},"issue":"8"}]