[{"year":"2015","page":"1-13","citation":{"ama":"Walter O, Haeb-Umbach R, Mokbel B, Paassen B, Hammer B. Autonomous Learning of Representations. <i>KI - Kuenstliche Intelligenz</i>. 2015:1-13. doi:<a href=\"http://dx.doi.org/10.1007/s13218-015-0372-1\">http://dx.doi.org/10.1007/s13218-015-0372-1</a>","ieee":"O. Walter, R. Haeb-Umbach, B. Mokbel, B. Paassen, and B. Hammer, “Autonomous Learning of Representations,” <i>KI - Kuenstliche Intelligenz</i>, pp. 1–13, 2015.","chicago":"Walter, Oliver, Reinhold Haeb-Umbach, Bassam Mokbel, Benjamin Paassen, and Barbara Hammer. “Autonomous Learning of Representations.” <i>KI - Kuenstliche Intelligenz</i>, 2015, 1–13. <a href=\"http://dx.doi.org/10.1007/s13218-015-0372-1\">http://dx.doi.org/10.1007/s13218-015-0372-1</a>.","apa":"Walter, O., Haeb-Umbach, R., Mokbel, B., Paassen, B., &#38; Hammer, B. (2015). Autonomous Learning of Representations. <i>KI - Kuenstliche Intelligenz</i>, 1–13. <a href=\"http://dx.doi.org/10.1007/s13218-015-0372-1\">http://dx.doi.org/10.1007/s13218-015-0372-1</a>","bibtex":"@article{Walter_Haeb-Umbach_Mokbel_Paassen_Hammer_2015, title={Autonomous Learning of Representations}, DOI={<a href=\"http://dx.doi.org/10.1007/s13218-015-0372-1\">http://dx.doi.org/10.1007/s13218-015-0372-1</a>}, journal={KI - Kuenstliche Intelligenz}, author={Walter, Oliver and Haeb-Umbach, Reinhold and Mokbel, Bassam and Paassen, Benjamin and Hammer, Barbara}, year={2015}, pages={1–13} }","mla":"Walter, Oliver, et al. “Autonomous Learning of Representations.” <i>KI - Kuenstliche Intelligenz</i>, 2015, pp. 1–13, doi:<a href=\"http://dx.doi.org/10.1007/s13218-015-0372-1\">http://dx.doi.org/10.1007/s13218-015-0372-1</a>.","short":"O. Walter, R. Haeb-Umbach, B. Mokbel, B. Paassen, B. Hammer, KI - Kuenstliche Intelligenz (2015) 1–13."},"oa":"1","date_updated":"2022-01-06T06:51:12Z","author":[{"full_name":"Walter, Oliver","last_name":"Walter","first_name":"Oliver"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"},{"first_name":"Bassam","full_name":"Mokbel, Bassam","last_name":"Mokbel"},{"first_name":"Benjamin","full_name":"Paassen, Benjamin","last_name":"Paassen"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"}],"date_created":"2019-07-12T05:30:51Z","title":"Autonomous Learning of Representations","doi":"http://dx.doi.org/10.1007/s13218-015-0372-1","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2015/WaHaMoPaHa15.pdf"}],"publication":"KI - Kuenstliche Intelligenz","type":"journal_article","abstract":[{"lang":"eng","text":"Besides the core learning algorithm itself, one major question in machine learning is how to best encode given training data such that the learning technology can efficiently learn based thereon and generalize to novel data. While classical approaches often rely on a hand coded data representation, the topic of autonomous representation or feature learning plays a major role in modern learning architectures. The goal of this contribution is to give an overview about different principles of autonomous feature learning, and to exemplify two principles based on two recent examples: autonomous metric learning for sequences, and autonomous learning of a deep representation for spoken language, respectively."}],"status":"public","_id":"11922","department":[{"_id":"54"}],"user_id":"44006","keyword":["Representation learning","Metric learning","Deep representation","Spoken language"],"language":[{"iso":"eng"}]},{"author":[{"first_name":"Oliver","last_name":"Walter","full_name":"Walter, Oliver"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"},{"first_name":"Jan","last_name":"Strunk","full_name":"Strunk, Jan"},{"last_name":"P. Himmelmann","full_name":"P. Himmelmann, Nikolaus ","first_name":"Nikolaus "}],"date_created":"2019-07-12T05:30:52Z","date_updated":"2022-01-06T06:51:12Z","oa":"1","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2015/WaHaStHi.pdf","open_access":"1"}],"title":"Lexicon Discovery for Language Preservation using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01)","citation":{"short":"O. Walter, R. Haeb-Umbach, J. Strunk, N. P. Himmelmann, Lexicon Discovery for Language Preservation Using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01), 2015.","bibtex":"@book{Walter_Haeb-Umbach_Strunk_P. Himmelmann_2015, title={Lexicon Discovery for Language Preservation using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01)}, author={Walter, Oliver and Haeb-Umbach, Reinhold and Strunk, Jan and P. Himmelmann, Nikolaus }, year={2015} }","mla":"Walter, Oliver, et al. <i>Lexicon Discovery for Language Preservation Using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01)</i>. 2015.","apa":"Walter, O., Haeb-Umbach, R., Strunk, J., &#38; P. Himmelmann, N. (2015). <i>Lexicon Discovery for Language Preservation using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01)</i>.","ieee":"O. Walter, R. Haeb-Umbach, J. Strunk, and N. P. Himmelmann, <i>Lexicon Discovery for Language Preservation using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01)</i>. 2015.","chicago":"Walter, Oliver, Reinhold Haeb-Umbach, Jan Strunk, and Nikolaus  P. Himmelmann. <i>Lexicon Discovery for Language Preservation Using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01)</i>, 2015.","ama":"Walter O, Haeb-Umbach R, Strunk J, P. Himmelmann N. <i>Lexicon Discovery for Language Preservation Using Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01)</i>.; 2015."},"year":"2015","department":[{"_id":"54"}],"user_id":"44006","_id":"11923","language":[{"iso":"eng"}],"type":"report","status":"public","abstract":[{"text":"In this paper we show that recently developed algorithms for unsupervised word segmentation can be a valuable tool for the documentation of endangered languages. We applied an unsupervised word segmentation algorithm based on a nested Pitman-Yor language model to two austronesian languages, Wooi and Waima'a. The algorithm was then modified and parameterized to cater the needs of linguists for high precision of lexical discovery: We obtained a lexicon precision of of 69.2\\% and 67.5\\% for Wooi and Waima'a, respectively, if single-letter words and words found less than three times were discarded. A comparison with an English word segmentation task showed comparable performance, verifying that the assumptions underlying the Pitman-Yor language model, the universality of Zipf's law and the power of n-gram structures, do also hold for languages as exotic as Wooi and Waima'a.","lang":"eng"}]},{"quality_controlled":"1","year":"2015","citation":{"chicago":"Hoang, Manh Kha, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Aligning Training Models with Smartphone Properties in WiFi Fingerprinting Based Indoor Localization.” In <i>40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)</i>, 2015.","ieee":"M. K. Hoang, J. Schmalenstroeer, and R. Haeb-Umbach, “Aligning training models with smartphone properties in WiFi fingerprinting based indoor localization,” 2015.","ama":"Hoang MK, Schmalenstroeer J, Haeb-Umbach R. Aligning training models with smartphone properties in WiFi fingerprinting based indoor localization. In: <i>40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)</i>. ; 2015.","apa":"Hoang, M. K., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2015). Aligning training models with smartphone properties in WiFi fingerprinting based indoor localization. <i>40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)</i>.","short":"M.K. Hoang, J. Schmalenstroeer, R. Haeb-Umbach, in: 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015.","bibtex":"@inproceedings{Hoang_Schmalenstroeer_Haeb-Umbach_2015, title={Aligning training models with smartphone properties in WiFi fingerprinting based indoor localization}, booktitle={40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)}, author={Hoang, Manh Kha and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2015} }","mla":"Hoang, Manh Kha, et al. “Aligning Training Models with Smartphone Properties in WiFi Fingerprinting Based Indoor Localization.” <i>40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)</i>, 2015."},"oa":"1","date_updated":"2023-10-26T08:11:43Z","date_created":"2019-07-12T05:29:55Z","author":[{"full_name":"Hoang, Manh Kha","last_name":"Hoang","first_name":"Manh Kha"},{"first_name":"Joerg","last_name":"Schmalenstroeer","id":"460","full_name":"Schmalenstroeer, Joerg"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"title":"Aligning training models with smartphone properties in WiFi fingerprinting based indoor localization","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2015/HoSchHa2015.pdf"}],"type":"conference","publication":"40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)","status":"public","_id":"11874","user_id":"460","department":[{"_id":"54"}],"language":[{"iso":"eng"}]},{"oa":"1","date_updated":"2022-01-06T06:51:08Z","author":[{"full_name":"Chinaev, Aleksej","last_name":"Chinaev","first_name":"Aleksej"},{"first_name":"Marc","full_name":"Puels, Marc","last_name":"Puels"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_created":"2019-07-12T05:27:27Z","title":"Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2014/ChPuHa2014.pdf"}],"related_material":{"link":[{"relation":"supplementary_material","description":"Presentation","url":"https://groups.uni-paderborn.de/nt/pubs/2014/ChPuHa2014_Talk.pdf"}]},"year":"2014","citation":{"apa":"Chinaev, A., Puels, M., &#38; Haeb-Umbach, R. (2014). Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR. In <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>.","short":"A. Chinaev, M. Puels, R. Haeb-Umbach, in: 11. ITG Fachtagung Sprachkommunikation (ITG 2014), 2014.","bibtex":"@inproceedings{Chinaev_Puels_Haeb-Umbach_2014, title={Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR}, booktitle={11. ITG Fachtagung Sprachkommunikation (ITG 2014)}, author={Chinaev, Aleksej and Puels, Marc and Haeb-Umbach, Reinhold}, year={2014} }","mla":"Chinaev, Aleksej, et al. “Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR.” <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014.","chicago":"Chinaev, Aleksej, Marc Puels, and Reinhold Haeb-Umbach. “Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR.” In <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014.","ieee":"A. Chinaev, M. Puels, and R. Haeb-Umbach, “Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR,” in <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014.","ama":"Chinaev A, Puels M, Haeb-Umbach R. Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR. In: <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>. ; 2014."},"_id":"11746","user_id":"44006","department":[{"_id":"54"}],"language":[{"iso":"eng"}],"type":"conference","publication":"11. ITG Fachtagung Sprachkommunikation (ITG 2014)","abstract":[{"lang":"eng","text":" \"A method for nonstationary noise robust automatic speech recognition (ASR) is to first estimate the changing noise statistics and second clean up the features prior to recognition accordingly. Here, the first is accomplished by noise tracking in the spectral domain, while the second relies on Bayesian enhancement in the feature domain. In this way we take advantage of our recently proposed maximum a-posteriori based (MAP-B) noise power spectral density estimation algorithm, which is able to estimate the noise statistics even in time-frequency bins dominated by speech. We show that MAP-B noise tracking leads to an improved noise model estimate in the feature domain compared to estimating noise in speech absence periods only, if the bias resulting from the nonlinear transformation from the spectral to the feature domain is accounted for. Consequently, ASR results are improved, as is shown by experiments conducted on the Aurora IV database.\" "}],"status":"public"},{"oa":"1","date_updated":"2022-01-06T06:51:08Z","date_created":"2019-07-12T05:27:34Z","author":[{"first_name":"Lukas","full_name":"Drude, Lukas","id":"11213","last_name":"Drude"},{"full_name":"Chinaev, Aleksej","last_name":"Chinaev","first_name":"Aleksej"},{"full_name":"Tran Vu, Dang Hai","last_name":"Tran Vu","first_name":"Dang Hai"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"title":"Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHa2014.pdf"}],"related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHa2014_Poster.pdf"}]},"year":"2014","citation":{"bibtex":"@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models}, booktitle={39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2014} }","short":"L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.","mla":"Drude, Lukas, et al. “Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models.” <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","apa":"Drude, L., Chinaev, A., Tran Vu, D. H., &#38; Haeb-Umbach, R. (2014). Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models. In <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>.","ieee":"L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models,” in <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","chicago":"Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models.” In <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","ama":"Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models. In: <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>. ; 2014."},"_id":"11752","department":[{"_id":"54"}],"user_id":"44006","language":[{"iso":"eng"}],"publication":"39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)","type":"conference","abstract":[{"text":" \"In this contribution we derive a variational EM (VEM) algorithm for model selection in complex Watson mixture models, which have been recently proposed as a model of the distribution of normalized microphone array signals in the short-time Fourier transform domain. The VEM algorithm is applied to count the number of active sources in a speech mixture by iteratively estimating the mode vectors of the Watson distributions and suppressing the signals from the corresponding directions. A key theoretical contribution is the derivation of the MMSE estimate of a quadratic form involving the mode vector of the Watson distribution. The experimental results demonstrate the effectiveness of the source counting approach at moderately low SNR. It is further shown that the VEM algorithm is more robust w.r.t. used threshold values.\" ","lang":"eng"}],"status":"public"},{"date_created":"2019-07-12T05:27:35Z","author":[{"last_name":"Drude","full_name":"Drude, Lukas","id":"11213","first_name":"Lukas"},{"full_name":"Chinaev, Aleksej","last_name":"Chinaev","first_name":"Aleksej"},{"first_name":"Dang Hai","full_name":"Tran Vu, Dang Hai","last_name":"Tran Vu"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"date_updated":"2022-01-06T06:51:08Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14.pdf"}],"title":"Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models","related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14_Poster.pdf"}]},"page":"213-217","citation":{"bibtex":"@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models}, booktitle={14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2014}, pages={213–217} }","short":"L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.","mla":"Drude, Lukas, et al. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>, 2014, pp. 213–17.","apa":"Drude, L., Chinaev, A., Tran Vu, D. H., &#38; Haeb-Umbach, R. (2014). Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i> (pp. 213–217).","ama":"Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In: <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>. ; 2014:213-217.","chicago":"Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” In <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>, 213–17, 2014.","ieee":"L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models,” in <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>, 2014, pp. 213–217."},"year":"2014","department":[{"_id":"54"}],"user_id":"44006","_id":"11753","language":[{"iso":"eng"}],"keyword":["Accuracy","Acoustics","Estimation","Mathematical model","Soruce separation","Speech","Vectors","Bayes methods","Blind source separation","Directional statistics","Number of speakers","Speaker diarization"],"publication":"14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)","type":"conference","status":"public","abstract":[{"lang":"eng","text":"This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data."}]},{"main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2014/HeWaHa2014.pdf"}],"title":"Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices","date_created":"2019-07-12T05:28:46Z","author":[{"last_name":"Heymann","full_name":"Heymann, Jahn","id":"9168","first_name":"Jahn"},{"first_name":"Oliver","full_name":"Walter, Oliver","last_name":"Walter"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"},{"first_name":"Bhiksha","last_name":"Raj","full_name":"Raj, Bhiksha"}],"oa":"1","date_updated":"2022-01-06T06:51:09Z","citation":{"short":"J. Heymann, O. Walter, R. Haeb-Umbach, B. Raj, in: 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.","mla":"Heymann, Jahn, et al. “Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices.” <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","bibtex":"@inproceedings{Heymann_Walter_Haeb-Umbach_Raj_2014, title={Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices}, booktitle={39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)}, author={Heymann, Jahn and Walter, Oliver and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2014} }","apa":"Heymann, J., Walter, O., Haeb-Umbach, R., &#38; Raj, B. (2014). Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices. In <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>.","chicago":"Heymann, Jahn, Oliver Walter, Reinhold Haeb-Umbach, and Bhiksha Raj. “Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices.” In <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","ieee":"J. Heymann, O. Walter, R. Haeb-Umbach, and B. Raj, “Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices,” in <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","ama":"Heymann J, Walter O, Haeb-Umbach R, Raj B. Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices. In: <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>. ; 2014."},"year":"2014","related_material":{"link":[{"description":"Poster","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2014/HeWaHa2014_Poster.pdf"}]},"language":[{"iso":"eng"}],"user_id":"44006","department":[{"_id":"54"}],"_id":"11814","status":"public","abstract":[{"lang":"eng","text":" \"In this paper we present an algorithm for the unsupervised segmentation of a lattice produced by a phoneme recognizer into words. Using a lattice rather than a single phoneme string accounts for the uncertainty of the recognizer about the true label sequence. An example application is the discovery of lexical units from the output of an error-prone phoneme recognizer in a zero-resource setting, where neither the lexicon nor the language model (LM) is known. We propose a computationally efficient iterative approach, which alternates between the following two steps: First, the most probable string is extracted from the lattice using a phoneme LM learned on the segmentation result of the previous iteration. Second, word segmentation is performed on the extracted string using a word and phoneme LM which is learned alongside the new segmentation. We present results on lattices produced by a phoneme recognizer on the WSJCAM0 dataset. We show that our approach delivers superior segmentation performance than an earlier approach found in the literature, in particular for higher-order language models. \" "}],"type":"conference","publication":"39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)"},{"language":[{"iso":"eng"}],"_id":"11831","department":[{"_id":"54"}],"user_id":"44006","abstract":[{"lang":"eng","text":" \"Several self-localization algorithms have been proposed, that determine the positions of either acoustic or visual sensors autonomously. Usually these positions are given in a modality specific coordinate system, with an unknown rotation, translation and scale between the different systems. For a joint audiovisual tracking, where the different modalities support each other, the two modalities need to be mapped into a common coordinate system. In this paper we propose to estimate this mapping based on audiovisual correlates, i.e., a speaker that can be localized by both, a microphone and a camera network separately. The voice is tracked by a microphone network, which had to be calibrated by a self-localization algorithm at first, and the head is tracked by a calibrated camera network. Unlike existing Singular Value Decomposition based approaches to estimate the coordinate system mapping, we propose to perform an estimation in the shape domain, which turns out to be computationally more efficient. Simulations of the self-localization of an acoustic sensor network and a following coordinate mapping for a joint speaker localization showed a significant improvement of the localization performance, since the modalities were able to support each other.\" "}],"status":"public","publication":"11. ITG Fachtagung Sprachkommunikation (ITG 2014)","type":"conference","title":"Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2014/JaHa2014.pdf","open_access":"1"}],"date_updated":"2022-01-06T06:51:11Z","oa":"1","date_created":"2019-07-12T05:29:06Z","author":[{"full_name":"Jacob, Florian","last_name":"Jacob","first_name":"Florian"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"year":"2014","citation":{"short":"F. Jacob, R. Haeb-Umbach, in: 11. ITG Fachtagung Sprachkommunikation (ITG 2014), 2014.","bibtex":"@inproceedings{Jacob_Haeb-Umbach_2014, title={Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking}, booktitle={11. ITG Fachtagung Sprachkommunikation (ITG 2014)}, author={Jacob, Florian and Haeb-Umbach, Reinhold}, year={2014} }","mla":"Jacob, Florian, and Reinhold Haeb-Umbach. “Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking.” <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014.","apa":"Jacob, F., &#38; Haeb-Umbach, R. (2014). Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking. In <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>.","ama":"Jacob F, Haeb-Umbach R. Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking. In: <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>. ; 2014.","ieee":"F. Jacob and R. Haeb-Umbach, “Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking,” in <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014.","chicago":"Jacob, Florian, and Reinhold Haeb-Umbach. “Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking.” In <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014."},"related_material":{"link":[{"description":"Presentation","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2014/JaHa2014_Talk.pdf"}]}},{"date_updated":"2022-01-06T06:51:11Z","volume":22,"author":[{"first_name":"Volker","last_name":"Leutnant","full_name":"Leutnant, Volker"},{"first_name":"Alexander","last_name":"Krueger","full_name":"Krueger, Alexander"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold"}],"date_created":"2019-07-12T05:29:41Z","title":"A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech","doi":"10.1109/TASLP.2013.2285480","publication_identifier":{"issn":["2329-9290"]},"issue":"1","year":"2014","intvolume":"        22","page":"95-109","citation":{"ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech,” <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>, vol. 22, no. 1, pp. 95–109, 2014.","chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i> 22, no. 1 (2014): 95–109. <a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">https://doi.org/10.1109/TASLP.2013.2285480</a>.","ama":"Leutnant V, Krueger A, Haeb-Umbach R. A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech. <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>. 2014;22(1):95-109. doi:<a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">10.1109/TASLP.2013.2285480</a>","apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2014). A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech. <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>, <i>22</i>(1), 95–109. <a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">https://doi.org/10.1109/TASLP.2013.2285480</a>","mla":"Leutnant, Volker, et al. “A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>, vol. 22, no. 1, 2014, pp. 95–109, doi:<a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">10.1109/TASLP.2013.2285480</a>.","bibtex":"@article{Leutnant_Krueger_Haeb-Umbach_2014, title={A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech}, volume={22}, DOI={<a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">10.1109/TASLP.2013.2285480</a>}, number={1}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2014}, pages={95–109} }","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE/ACM Transactions on Audio, Speech, and Language Processing 22 (2014) 95–109."},"_id":"11861","department":[{"_id":"54"}],"user_id":"44006","keyword":["computational complexity","reverberation","speech recognition","automatic speech recognition","background noise","clean speech","computational complexity","energy compensation","logarithmic mel power spectral domain","mel frequency cepstral coefficients","microphone input signals","model-based feature compensation schemes","noisy reverberant speech automatic recognition","noisy reverberant speech features","reverberation","Atmospheric modeling","Computational modeling","Noise","Noise measurement","Reverberation","Speech","Vectors","Model-based feature compensation","observation model for reverberant and noisy speech","recursive observation model","robust automatic speech recognition"],"language":[{"iso":"eng"}],"publication":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","type":"journal_article","abstract":[{"lang":"eng","text":"In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data."}],"status":"public"},{"issue":"4","citation":{"chicago":"Li, Jinyu, Li Deng, Yifan Gong, and Reinhold Haeb-Umbach. “An Overview of Noise-Robust Automatic Speech Recognition.” <i>IEEE Transactions on Audio, Speech and Language Processing</i> 22, no. 4 (2014): 745–77. <a href=\"https://doi.org/10.1109/TASLP.2014.2304637\">https://doi.org/10.1109/TASLP.2014.2304637</a>.","ieee":"J. Li, L. Deng, Y. Gong, and R. Haeb-Umbach, “An Overview of Noise-Robust Automatic Speech Recognition,” <i>IEEE Transactions on Audio, Speech and Language Processing</i>, vol. 22, no. 4, pp. 745–777, 2014.","ama":"Li J, Deng L, Gong Y, Haeb-Umbach R. An Overview of Noise-Robust Automatic Speech Recognition. <i>IEEE Transactions on Audio, Speech and Language Processing</i>. 2014;22(4):745-777. doi:<a href=\"https://doi.org/10.1109/TASLP.2014.2304637\">10.1109/TASLP.2014.2304637</a>","apa":"Li, J., Deng, L., Gong, Y., &#38; Haeb-Umbach, R. (2014). An Overview of Noise-Robust Automatic Speech Recognition. <i>IEEE Transactions on Audio, Speech and Language Processing</i>, <i>22</i>(4), 745–777. <a href=\"https://doi.org/10.1109/TASLP.2014.2304637\">https://doi.org/10.1109/TASLP.2014.2304637</a>","bibtex":"@article{Li_Deng_Gong_Haeb-Umbach_2014, title={An Overview of Noise-Robust Automatic Speech Recognition}, volume={22}, DOI={<a href=\"https://doi.org/10.1109/TASLP.2014.2304637\">10.1109/TASLP.2014.2304637</a>}, number={4}, journal={IEEE Transactions on Audio, Speech and Language Processing}, author={Li, Jinyu and Deng, Li and Gong, Yifan and Haeb-Umbach, Reinhold}, year={2014}, pages={745–777} }","mla":"Li, Jinyu, et al. “An Overview of Noise-Robust Automatic Speech Recognition.” <i>IEEE Transactions on Audio, Speech and Language Processing</i>, vol. 22, no. 4, 2014, pp. 745–77, doi:<a href=\"https://doi.org/10.1109/TASLP.2014.2304637\">10.1109/TASLP.2014.2304637</a>.","short":"J. Li, L. Deng, Y. Gong, R. Haeb-Umbach, IEEE Transactions on Audio, Speech and Language Processing 22 (2014) 745–777."},"intvolume":"        22","page":"745-777","year":"2014","date_created":"2019-07-12T05:29:47Z","author":[{"full_name":"Li, Jinyu","last_name":"Li","first_name":"Jinyu"},{"first_name":"Li","last_name":"Deng","full_name":"Deng, Li"},{"full_name":"Gong, Yifan","last_name":"Gong","first_name":"Yifan"},{"full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"volume":22,"date_updated":"2022-01-06T06:51:11Z","oa":"1","main_file_link":[{"url":"http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6732927","open_access":"1"}],"doi":"10.1109/TASLP.2014.2304637","title":"An Overview of Noise-Robust Automatic Speech Recognition","type":"journal_article","publication":"IEEE Transactions on Audio, Speech and Language Processing","status":"public","abstract":[{"lang":"eng","text":"New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed."}],"user_id":"44006","department":[{"_id":"54"}],"_id":"11867","language":[{"iso":"eng"}],"keyword":["Speech recognition","compensation","distortion modeling","joint model training","noise","robustness","uncertainty processing"]},{"publication":"INTERSPEECH 2014","type":"conference","abstract":[{"text":"In this paper, we investigate unsupervised acoustic model training approaches for dysarthric-speech recognition. These models are first, frame-based Gaussian posteriorgrams, obtained from Vector Quantization (VQ), second, so-called Acoustic Unit Descriptors (AUDs), which are hidden Markov models of phone-like units, that are trained in an unsupervised fashion, and, third, posteriorgrams computed on the AUDs. Experiments were carried out on a database collected from a home automation task and containing nine speakers, of which seven are considered to utter dysarthric speech. All unsupervised modeling approaches delivered significantly better recognition rates than a speaker-independent phoneme recognition baseline, showing the suitability of unsupervised acoustic model training for dysarthric speech. While the AUD models led to the most compact representation of an utterance for the subsequent semantic inference stage, posteriorgram-based representations resulted in higher recognition rates, with the Gaussian posteriorgram achieving the highest slot filling F-score of 97.02%. Index Terms: unsupervised learning, acoustic unit descriptors, dysarthric speech, non-negative matrix factorization","lang":"eng"}],"status":"public","_id":"11918","department":[{"_id":"54"}],"user_id":"44006","language":[{"iso":"eng"}],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2014/WaDeHaebGeOnVa14_Poster.pdf","description":"Poster","relation":"supplementary_material"},{"relation":"supplementary_material","description":"Spotlight","url":"https://groups.uni-paderborn.de/nt/pubs/2014/WaDeHaebGeOnVa14_Spotlight.pdf"}]},"year":"2014","citation":{"apa":"Walter, O., Despotovic, V., Haeb-Umbach, R., Gemmeke, J., Ons, B., &#38; Van hamme, H. (2014). An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface. In <i>INTERSPEECH 2014</i>.","mla":"Walter, Oliver, et al. “An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface.” <i>INTERSPEECH 2014</i>, 2014.","bibtex":"@inproceedings{Walter_Despotovic_Haeb-Umbach_Gemmeke_Ons_Van hamme_2014, title={An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface}, booktitle={INTERSPEECH 2014}, author={Walter, Oliver and Despotovic, Vladimir and Haeb-Umbach, Reinhold and Gemmeke, Jrt and Ons, Bart and Van hamme, Hugo}, year={2014} }","short":"O. Walter, V. Despotovic, R. Haeb-Umbach, J. Gemmeke, B. Ons, H. Van hamme, in: INTERSPEECH 2014, 2014.","chicago":"Walter, Oliver, Vladimir Despotovic, Reinhold Haeb-Umbach, Jrt Gemmeke, Bart Ons, and Hugo Van hamme. “An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface.” In <i>INTERSPEECH 2014</i>, 2014.","ieee":"O. Walter, V. Despotovic, R. Haeb-Umbach, J. Gemmeke, B. Ons, and H. Van hamme, “An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface,” in <i>INTERSPEECH 2014</i>, 2014.","ama":"Walter O, Despotovic V, Haeb-Umbach R, Gemmeke J, Ons B, Van hamme H. An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface. In: <i>INTERSPEECH 2014</i>. ; 2014."},"oa":"1","date_updated":"2022-01-06T06:51:12Z","date_created":"2019-07-12T05:30:46Z","author":[{"full_name":"Walter, Oliver","last_name":"Walter","first_name":"Oliver"},{"first_name":"Vladimir","full_name":"Despotovic, Vladimir","last_name":"Despotovic"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"},{"first_name":"Jrt","full_name":"Gemmeke, Jrt","last_name":"Gemmeke"},{"first_name":"Bart","full_name":"Ons, Bart","last_name":"Ons"},{"last_name":"Van hamme","full_name":"Van hamme, Hugo","first_name":"Hugo"}],"title":"An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2014/WaDeHaebGeOnVa14.pdf","open_access":"1"}]},{"language":[{"iso":"eng"}],"keyword":["Gossip algorithm"],"publication":"Signal Processing","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"}],"date_created":"2019-07-12T05:30:23Z","title":"A combined hardware-software approach for acoustic sensor network synchronization ","issue":"0","quality_controlled":"1","year":"2014","user_id":"460","department":[{"_id":"54"}],"_id":"11898","type":"journal_article","status":"public","author":[{"last_name":"Schmalenstroeer","full_name":"Schmalenstroeer, Joerg","id":"460","first_name":"Joerg"},{"last_name":"Jebramcik","full_name":"Jebramcik, Patrick","first_name":"Patrick"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_updated":"2023-10-26T08:11:22Z","oa":"1","main_file_link":[{"open_access":"1","url":"http://www.sciencedirect.com/science/article/pii/S0165168414002990"}],"doi":"http://dx.doi.org/10.1016/j.sigpro.2014.06.030","publication_identifier":{"issn":["0165-1684"]},"citation":{"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>.","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>","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>","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} }","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>.","short":"J. Schmalenstroeer, P. Jebramcik, R. Haeb-Umbach, Signal Processing (2014)."},"page":" - "},{"abstract":[{"text":" \"In this paper we present an approach for synchronizing the sampling clocks of distributed microphones over a wireless network. The proposed system uses a two stage procedure. It first employs a two-way message exchange algorithm to estimate the clock phase and frequency difference between two nodes and then uses a gossiping algorithmto estimate a virtual master clock, to which all sensor nodes synchronize. Simulation results are presented for networks of different topology and size, showing the effectiveness of our approach.\" ","lang":"eng"}],"status":"public","type":"conference","publication":"39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)","language":[{"iso":"eng"}],"_id":"11897","user_id":"460","department":[{"_id":"54"}],"year":"2014","citation":{"bibtex":"@inproceedings{Schmalenstroeer_Jebramcik_Haeb-Umbach_2014, title={A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks}, booktitle={39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)}, author={Schmalenstroeer, Joerg and Jebramcik, Patrick and Haeb-Umbach, Reinhold}, year={2014} }","short":"J. Schmalenstroeer, P. Jebramcik, R. Haeb-Umbach, in: 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.","mla":"Schmalenstroeer, Joerg, et al. “A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks.” <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","apa":"Schmalenstroeer, J., Jebramcik, P., &#38; Haeb-Umbach, R. (2014). A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks. <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>.","ieee":"J. Schmalenstroeer, P. Jebramcik, and R. Haeb-Umbach, “A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks,” 2014.","chicago":"Schmalenstroeer, Joerg, Patrick Jebramcik, and Reinhold Haeb-Umbach. “A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks.” In <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>, 2014.","ama":"Schmalenstroeer J, Jebramcik P, Haeb-Umbach R. A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks. In: <i>39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)</i>. ; 2014."},"quality_controlled":"1","related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebICASSP2014_Poster.pdf","relation":"supplementary_material","description":"Poster"}]},"title":"A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2014/SchHae2014.pdf"}],"oa":"1","date_updated":"2023-10-26T08:11:31Z","author":[{"first_name":"Joerg","last_name":"Schmalenstroeer","full_name":"Schmalenstroeer, Joerg","id":"460"},{"first_name":"Patrick","full_name":"Jebramcik, Patrick","last_name":"Jebramcik"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_created":"2019-07-12T05:30:22Z"},{"language":[{"iso":"eng"}],"_id":"11903","user_id":"460","department":[{"_id":"54"}],"abstract":[{"text":"\"Acoustic sensor network clock synchronization via time stamp exchange between the sensor nodes is not accurate enough for many acoustic signal processing tasks, such as speaker localization. To improve synchronization accuracy it has therefore been proposed to employ a Kalman Filter to obtain improved frequency deviation and phase offset estimates. The estimation requires a statistical model of the errors of the measurements obtained from the time stamp exchange algorithm. These errors are caused by random transmission delays and hardware effects and are thus network specific. In this contribution we develop an algorithm to estimate the parameters of the measurement error model alongside the Kalman filter based sampling clock synchronization, employing the Expectation Maximization algorithm. Simulation results demonstrate that the online estimation of the error model parameters leads only to a small degradation of the synchronization performance compared to a perfectly known observation error model.\"","lang":"eng"}],"status":"public","type":"conference","publication":"11. ITG Fachtagung Sprachkommunikation (ITG 2014)","title":"Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebITG2014.pdf","open_access":"1"}],"oa":"1","date_updated":"2023-10-26T08:14:00Z","date_created":"2019-07-12T05:30:29Z","author":[{"first_name":"Joerg","full_name":"Schmalenstroeer, Joerg","id":"460","last_name":"Schmalenstroeer"},{"full_name":"Zhao, Weile","last_name":"Zhao","first_name":"Weile"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"year":"2014","citation":{"short":"J. Schmalenstroeer, W. Zhao, R. Haeb-Umbach, in: 11. ITG Fachtagung Sprachkommunikation (ITG 2014), 2014.","mla":"Schmalenstroeer, Joerg, et al. “Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization.” <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014.","bibtex":"@inproceedings{Schmalenstroeer_Zhao_Haeb-Umbach_2014, title={Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization}, booktitle={11. ITG Fachtagung Sprachkommunikation (ITG 2014)}, author={Schmalenstroeer, Joerg and Zhao, Weile and Haeb-Umbach, Reinhold}, year={2014} }","apa":"Schmalenstroeer, J., Zhao, W., &#38; Haeb-Umbach, R. (2014). Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization. <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>.","ieee":"J. Schmalenstroeer, W. Zhao, and R. Haeb-Umbach, “Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization,” 2014.","chicago":"Schmalenstroeer, Joerg, Weile Zhao, and Reinhold Haeb-Umbach. “Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization.” In <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>, 2014.","ama":"Schmalenstroeer J, Zhao W, Haeb-Umbach R. Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization. In: <i>11. ITG Fachtagung Sprachkommunikation (ITG 2014)</i>. ; 2014."},"quality_controlled":"1","related_material":{"link":[{"description":"Poster","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebITG2014_Poster.pdf"},{"description":"Demo","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebITG2014_Demo.pdf"}]}},{"status":"public","abstract":[{"text":"The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios.","lang":"eng"}],"publication":"Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on","type":"conference","language":[{"iso":"eng"}],"keyword":["Bayes methods","Gaussian processes","convolution","decision theory","decoding","noise","reverberation","speech coding","speech recognition","Bayesian decision rule","GMM","Gaussian mixture models","additive noise scenarios","automatic speech recognition systems","convolutive noise scenarios","decoding approach","mathematical framework","reverberant environments","significance decoding","speech feature estimation","uncertainty-of-observation techniques","Hidden Markov models","Maximum likelihood decoding","Noise","Speech","Speech recognition","Uncertainty","Uncertainty-of-observation","modified imputation","noise robust speech recognition","significance decoding","uncertainty decoding"],"department":[{"_id":"54"}],"user_id":"44006","_id":"11716","page":"6827-6831","citation":{"ieee":"A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based significance decoding,” in <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on</i>, 2013, pp. 6827–6831.","chicago":"Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant, and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>, 6827–31, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">https://doi.org/10.1109/ICASSP.2013.6638984</a>.","ama":"Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance decoding. In: <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>. ; 2013:6827-6831. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">10.1109/ICASSP.2013.6638984</a>","apa":"Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., &#38; Haeb-Umbach, R. (2013). GMM-based significance decoding. In <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on</i> (pp. 6827–6831). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">https://doi.org/10.1109/ICASSP.2013.6638984</a>","short":"A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On, 2013, pp. 6827–6831.","bibtex":"@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based significance decoding}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">10.1109/ICASSP.2013.6638984</a>}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831} }","mla":"Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>, 2013, pp. 6827–31, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">10.1109/ICASSP.2013.6638984</a>."},"year":"2013","publication_identifier":{"issn":["1520-6149"]},"doi":"10.1109/ICASSP.2013.6638984","title":"GMM-based significance decoding","author":[{"first_name":"Ahmed H.","last_name":"Abdelaziz","full_name":"Abdelaziz, Ahmed H."},{"first_name":"Steffen","full_name":"Zeiler, Steffen","last_name":"Zeiler"},{"first_name":"Dorothea","last_name":"Kolossa","full_name":"Kolossa, Dorothea"},{"first_name":"Volker","last_name":"Leutnant","full_name":"Leutnant, Volker"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_created":"2019-07-12T05:26:53Z","date_updated":"2022-01-06T06:51:07Z"},{"type":"conference","publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","status":"public","abstract":[{"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.","lang":"eng"}],"user_id":"44006","department":[{"_id":"54"}],"_id":"11740","language":[{"iso":"eng"}],"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"],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf","relation":"supplementary_material","description":"Poster"}]},"publication_identifier":{"issn":["1520-6149"]},"citation":{"ama":"Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In: <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>","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>.","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.","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>.","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>"},"page":"3352-3356","year":"2013","author":[{"last_name":"Chinaev","full_name":"Chinaev, Aleksej","first_name":"Aleksej"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_created":"2019-07-12T05:27:20Z","date_updated":"2022-01-06T06:51:08Z","oa":"1","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf","open_access":"1"}],"doi":"10.1109/ICASSP.2013.6638279","title":"MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations"},{"abstract":[{"lang":"eng","text":"In this paper we present an improved version of the recently proposed Maximum A-Posteriori (MAP) based noise power spectral density estimator. An empirical bias compensation and bandwidth adjustment reduce bias and variance of the noise variance estimates. The main advantage of the MAP-based postprocessor is its low estimation variance. The estimator is employed in the second stage of a two-stage single-channel speech enhancement system, where eight different state-of-the-art noise tracking algorithms were tested in the first stage. While the postprocessor hardly affects the results in stationary noise scenarios, it becomes the more effective the more nonstationary the noise is. The proposed postprocessor was able to improve all systems in babble noise w.r.t. the perceptual evaluation of speech quality performance."}],"status":"public","type":"conference","publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","language":[{"iso":"eng"}],"_id":"11742","user_id":"44006","department":[{"_id":"54"}],"year":"2013","citation":{"ama":"Chinaev A, Haeb-Umbach R, Taghia J, Martin R. Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor. In: <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:7477-7481. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6639116\">10.1109/ICASSP.2013.6639116</a>","ieee":"A. Chinaev, R. Haeb-Umbach, J. Taghia, and R. Martin, “Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor,” in <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 7477–7481.","chicago":"Chinaev, Aleksej, Reinhold Haeb-Umbach, Jalal Taghia, and Rainer Martin. “Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-Based Postprocessor.” In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 7477–81, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6639116\">https://doi.org/10.1109/ICASSP.2013.6639116</a>.","short":"A. Chinaev, R. Haeb-Umbach, J. Taghia, R. Martin, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 7477–7481.","mla":"Chinaev, Aleksej, et al. “Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-Based Postprocessor.” <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 7477–81, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6639116\">10.1109/ICASSP.2013.6639116</a>.","bibtex":"@inproceedings{Chinaev_Haeb-Umbach_Taghia_Martin_2013, title={Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6639116\">10.1109/ICASSP.2013.6639116</a>}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold and Taghia, Jalal and Martin, Rainer}, year={2013}, pages={7477–7481} }","apa":"Chinaev, A., Haeb-Umbach, R., Taghia, J., &#38; Martin, R. (2013). Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor. In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i> (pp. 7477–7481). <a href=\"https://doi.org/10.1109/ICASSP.2013.6639116\">https://doi.org/10.1109/ICASSP.2013.6639116</a>"},"page":"7477-7481","publication_identifier":{"issn":["1520-6149"]},"related_material":{"link":[{"description":"Poster","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHaTaRa13_Poster.pdf"}]},"title":"Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHaTaRa13.pdf","open_access":"1"}],"doi":"10.1109/ICASSP.2013.6639116","oa":"1","date_updated":"2022-01-06T06:51:08Z","date_created":"2019-07-12T05:27:23Z","author":[{"last_name":"Chinaev","full_name":"Chinaev, Aleksej","first_name":"Aleksej"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"},{"first_name":"Jalal","full_name":"Taghia, Jalal","last_name":"Taghia"},{"last_name":"Martin","full_name":"Martin, Rainer","first_name":"Rainer"}]},{"publication":"21th European Signal Processing Conference (EUSIPCO 2013)","type":"conference","status":"public","abstract":[{"text":"Among the different configurations of multi-microphone systems, e.g., in applications of speech dereverberation or denoising, we consider the case without a priori information of the microphone-array geometry. This naturally invokes explicit or implicit identification of source-receiver transfer functions as an indirect description of the microphone-array configuration. However, this blind channel identification (BCI) has been difficult due to the lack of unique identifiability in the presence of observation noise or near-common channel zeros. In this paper, we study the implicit BCI performance of blind signal enhancement techniques such as the adaptive principal component analysis (PCA) or the iterative blind equalization and channel identification (BENCH). To this end, we make use of a recently proposed metric, the normalized filter-projection misalignment (NFPM), which is tailored for BCI evaluation in ill-conditioned (e.g., noisy) scenarios. The resulting understanding of implicit BCI performance can help to judge the behavior of multi-microphone speech enhancement systems and the suitability of implicit BCI to serve channel-based (i.e., channel-informed) enhancement.","lang":"eng"}],"department":[{"_id":"54"}],"user_id":"44006","_id":"11762","language":[{"iso":"eng"}],"citation":{"apa":"Enzner, G., Schmid, D., &#38; Haeb-Umbach, R. (2013). On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques. In <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>.","bibtex":"@inproceedings{Enzner_Schmid_Haeb-Umbach_2013, title={On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques}, booktitle={21th European Signal Processing Conference (EUSIPCO 2013)}, author={Enzner, Gerald and Schmid, Dominic and Haeb-Umbach, Reinhold}, year={2013} }","mla":"Enzner, Gerald, et al. “On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques.” <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","short":"G. Enzner, D. Schmid, R. Haeb-Umbach, in: 21th European Signal Processing Conference (EUSIPCO 2013), 2013.","ama":"Enzner G, Schmid D, Haeb-Umbach R. On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques. In: <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>. ; 2013.","chicago":"Enzner, Gerald, Dominic Schmid, and Reinhold Haeb-Umbach. “On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques.” In <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","ieee":"G. Enzner, D. Schmid, and R. Haeb-Umbach, “On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques,” in <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013."},"year":"2013","date_created":"2019-07-12T05:27:46Z","author":[{"full_name":"Enzner, Gerald","last_name":"Enzner","first_name":"Gerald"},{"full_name":"Schmid, Dominic","last_name":"Schmid","first_name":"Dominic"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"oa":"1","date_updated":"2022-01-06T06:51:08Z","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/EnScHa2013.pdf","open_access":"1"}],"title":"On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques"},{"oa":"1","date_updated":"2022-01-06T06:51:09Z","author":[{"id":"9168","full_name":"Heymann, Jahn","last_name":"Heymann","first_name":"Jahn"},{"full_name":"Walter, Oliver","last_name":"Walter","first_name":"Oliver"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"},{"last_name":"Raj","full_name":"Raj, Bhiksha","first_name":"Bhiksha"}],"date_created":"2019-07-12T05:28:47Z","title":"Unsupervised Word Segmentation from Noisy Input","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HeWaHaRa13.pdf"}],"related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HeWaHaRa_Poster.pdf"}]},"year":"2013","citation":{"mla":"Heymann, Jahn, et al. “Unsupervised Word Segmentation from Noisy Input.” <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>, 2013.","bibtex":"@inproceedings{Heymann_Walter_Haeb-Umbach_Raj_2013, title={Unsupervised Word Segmentation from Noisy Input}, booktitle={Automatic Speech Recognition and Understanding Workshop (ASRU 2013)}, author={Heymann, Jahn and Walter, Oliver and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2013} }","short":"J. Heymann, O. Walter, R. Haeb-Umbach, B. Raj, in: Automatic Speech Recognition and Understanding Workshop (ASRU 2013), 2013.","apa":"Heymann, J., Walter, O., Haeb-Umbach, R., &#38; Raj, B. (2013). Unsupervised Word Segmentation from Noisy Input. In <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>.","chicago":"Heymann, Jahn, Oliver Walter, Reinhold Haeb-Umbach, and Bhiksha Raj. “Unsupervised Word Segmentation from Noisy Input.” In <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>, 2013.","ieee":"J. Heymann, O. Walter, R. Haeb-Umbach, and B. Raj, “Unsupervised Word Segmentation from Noisy Input,” in <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>, 2013.","ama":"Heymann J, Walter O, Haeb-Umbach R, Raj B. Unsupervised Word Segmentation from Noisy Input. In: <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>. ; 2013."},"_id":"11815","user_id":"44006","department":[{"_id":"54"}],"language":[{"iso":"eng"}],"type":"conference","publication":"Automatic Speech Recognition and Understanding Workshop (ASRU 2013)","status":"public"},{"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf","description":"Poster","relation":"supplementary_material"}]},"publication_identifier":{"issn":["1520-6149"]},"page":"3721-3725","citation":{"apa":"Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i> (pp. 3721–3725). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">https://doi.org/10.1109/ICASSP.2013.6638353</a>","mla":"Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3721–25, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>.","bibtex":"@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>}, booktitle={38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013}, pages={3721–3725} }","short":"M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.","ama":"Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In: <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>","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>.","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."},"year":"2013","date_created":"2019-07-12T05:28:48Z","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"}],"oa":"1","date_updated":"2022-01-06T06:51:09Z","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","publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","type":"conference","status":"public","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"}],"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"]}]