{"title":"Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization","_id":"11932","department":[{"_id":"54"}],"date_created":"2019-07-12T05:31:02Z","user_id":"44006","citation":{"short":"E. Warsitz, R. Haeb-Umbach, S. Peschke, in: International Conference on Spoken Language Processing (ICSLP 2004), 2004.","chicago":"Warsitz, Ernst, Reinhold Haeb-Umbach, and Sven Peschke. “Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization.” In <i>International Conference on Spoken Language Processing (ICSLP 2004)</i>, 2004.","bibtex":"@inproceedings{Warsitz_Haeb-Umbach_Peschke_2004, title={Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization}, booktitle={International Conference on Spoken Language Processing (ICSLP 2004)}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold and Peschke, Sven}, year={2004} }","apa":"Warsitz, E., Haeb-Umbach, R., &#38; Peschke, S. (2004). Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization. In <i>International Conference on Spoken Language Processing (ICSLP 2004)</i>.","ieee":"E. Warsitz, R. Haeb-Umbach, and S. Peschke, “Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization,” in <i>International Conference on Spoken Language Processing (ICSLP 2004)</i>, 2004.","ama":"Warsitz E, Haeb-Umbach R, Peschke S. Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization. In: <i>International Conference on Spoken Language Processing (ICSLP 2004)</i>. ; 2004.","mla":"Warsitz, Ernst, et al. “Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization.” <i>International Conference on Spoken Language Processing (ICSLP 2004)</i>, 2004."},"year":"2004","status":"public","oa":"1","abstract":[{"text":"While the main objective of adaptive Filter-and-Sum beamforming is to obtain an enhanced speech signal for subsequent processing like speech recognition, we show how speaker localization information can be derived from the filter coefficients. To increase localization accuracy, speaker tracking is performed by non-linear Bayesian state estimation, which is realized by sequential Monte Carlo methods. Improved acquisition and tracking performance was achieved even in highly reverberant environments, in comparison with both a Kalman Filter and a recently proposed Particle Filter operating on the output of a nonadaptive Delay-and-Sum beamformer.","lang":"eng"}],"author":[{"first_name":"Ernst","last_name":"Warsitz","full_name":"Warsitz, Ernst"},{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"},{"first_name":"Sven","full_name":"Peschke, Sven","last_name":"Peschke"}],"date_updated":"2022-01-06T06:51:12Z","type":"conference","publication":"International Conference on Spoken Language Processing (ICSLP 2004)","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2004/WaHaPe04.pdf"}],"language":[{"iso":"eng"}]}