[{"status":"public","abstract":[{"text":"Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel de-reverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.","lang":"eng"}],"publication":" IEEE Workshop on Applications of Signal Processing to Audio and Acoustics ","type":"conference","language":[{"iso":"eng"}],"keyword":["Reverberant speech","dereverberation","ASR","evaluation","challenge"],"department":[{"_id":"54"}],"user_id":"44006","_id":"11841","page":" 22-23 ","citation":{"mla":"Kinoshita, Keisuke, et al. “The Reverb Challenge: A Common Evaluation Framework for Dereverberation and Recognition of Reverberant Speech.” <i> IEEE Workshop on Applications of Signal Processing to Audio and Acoustics </i>, 2013, pp. 22–23.","short":"K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, E. Habets, R. Haeb-Umbach, V. Leutnant, A. Sehr, W. Kellermann, R. Maas, S. Gannot, B. Raj, in:  IEEE Workshop on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.","bibtex":"@inproceedings{Kinoshita_Delcroix_Yoshioka_Nakatani_Habets_Haeb-Umbach_Leutnant_Sehr_Kellermann_Maas_et al._2013, title={The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech}, booktitle={ IEEE Workshop on Applications of Signal Processing to Audio and Acoustics }, author={Kinoshita, Keisuke and Delcroix, Marc and Yoshioka, Takuya and Nakatani, Tomohiro and Habets, Emanuel and Haeb-Umbach, Reinhold and Leutnant, Volker and Sehr, Armin and Kellermann, Walter and Maas, Roland and et al.}, year={2013}, pages={22–23} }","apa":"Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Habets, E., Haeb-Umbach, R., … Raj, B. (2013). The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech. In <i> IEEE Workshop on Applications of Signal Processing to Audio and Acoustics </i> (pp. 22–23).","ama":"Kinoshita K, Delcroix M, Yoshioka T, et al. The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech. In: <i> IEEE Workshop on Applications of Signal Processing to Audio and Acoustics </i>. ; 2013:22-23.","ieee":"K. Kinoshita <i>et al.</i>, “The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech,” in <i> IEEE Workshop on Applications of Signal Processing to Audio and Acoustics </i>, 2013, pp. 22–23.","chicago":"Kinoshita, Keisuke, Marc Delcroix, Takuya Yoshioka, Tomohiro Nakatani, Emanuel Habets, Reinhold Haeb-Umbach, Volker Leutnant, et al. “The Reverb Challenge: A Common Evaluation Framework for Dereverberation and Recognition of Reverberant Speech.” In <i> IEEE Workshop on Applications of Signal Processing to Audio and Acoustics </i>, 22–23, 2013."},"year":"2013","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/Reverb2013.pdf","open_access":"1"}],"title":"The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech","author":[{"last_name":"Kinoshita","full_name":"Kinoshita, Keisuke","first_name":"Keisuke"},{"first_name":"Marc","last_name":"Delcroix","full_name":"Delcroix, Marc"},{"last_name":"Yoshioka","full_name":"Yoshioka, Takuya","first_name":"Takuya"},{"first_name":"Tomohiro","full_name":"Nakatani, Tomohiro","last_name":"Nakatani"},{"last_name":"Habets","full_name":"Habets, Emanuel","first_name":"Emanuel"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"},{"first_name":"Volker","last_name":"Leutnant","full_name":"Leutnant, Volker"},{"last_name":"Sehr","full_name":"Sehr, Armin","first_name":"Armin"},{"last_name":"Kellermann","full_name":"Kellermann, Walter","first_name":"Walter"},{"first_name":"Roland","full_name":"Maas, Roland","last_name":"Maas"},{"last_name":"Gannot","full_name":"Gannot, Sharon","first_name":"Sharon"},{"full_name":"Raj, Bhiksha","last_name":"Raj","first_name":"Bhiksha"}],"date_created":"2019-07-12T05:29:17Z","date_updated":"2022-01-06T06:51:11Z","oa":"1"},{"keyword":["Bayes methods","compensation","error statistics","reverberation","speech recognition","Bayesian feature enhancement","background noise","clean speech feature vectors","compensation","connected digits recognition task","error statistics","memory requirements","noisy reverberant data","posteriori probability density function","recursive formulation","reverberant logarithmic mel power spectral coefficients","robust automatic speech recognition","signal-to-noise ratios","time-variant observation","word error rate reduction","Robust automatic speech recognition","model-based Bayesian feature enhancement","observation model for reverberant and noisy speech","recursive observation model"],"language":[{"iso":"eng"}],"_id":"11862","user_id":"44006","department":[{"_id":"54"}],"abstract":[{"lang":"eng","text":"In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data."}],"status":"public","type":"journal_article","publication":"IEEE Transactions on Audio, Speech, and Language Processing","title":"Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition","doi":"10.1109/TASL.2013.2258013","date_updated":"2022-01-06T06:51:11Z","date_created":"2019-07-12T05:29:42Z","author":[{"first_name":"Volker","full_name":"Leutnant, Volker","last_name":"Leutnant"},{"full_name":"Krueger, Alexander","last_name":"Krueger","first_name":"Alexander"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"volume":21,"year":"2013","citation":{"short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 21 (2013) 1640–1652.","bibtex":"@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>}, number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2013}, pages={1640–1652} }","mla":"Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 21, no. 8, 2013, pp. 1640–52, doi:<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>.","apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2013). Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>21</i>(8), 1640–1652. <a href=\"https://doi.org/10.1109/TASL.2013.2258013\">https://doi.org/10.1109/TASL.2013.2258013</a>","ama":"Leutnant V, Krueger A, Haeb-Umbach R. Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2013;21(8):1640-1652. doi:<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>","chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 21, no. 8 (2013): 1640–52. <a href=\"https://doi.org/10.1109/TASL.2013.2258013\">https://doi.org/10.1109/TASL.2013.2258013</a>.","ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 21, no. 8, pp. 1640–1652, 2013."},"intvolume":"        21","page":"1640-1652","issue":"8"},{"_id":"11909","department":[{"_id":"54"}],"user_id":"44006","language":[{"iso":"eng"}],"publication":"21th European Signal Processing Conference (EUSIPCO 2013)","type":"conference","abstract":[{"lang":"eng","text":"We present a novel method to exploit correlations of adjacent time-frequency (TF)-slots for a sparseness-based blind speech separation (BSS) system. Usually, these correlations are exploited by some heuristic smoothing techniques in the post-processing of the estimated soft TF masks. We propose a different approach: Based on our previous work with one-dimensional (1D)-hidden Markov models (HMMs) along the time axis we extend the modeling to two-dimensional (2D)-HMMs to exploit both temporal and spectral correlations in the speech signal. Based on the principles of turbo decoding we solved the complex inference of 2D-HMMs by a modified forward-backward algorithm which operates alternatingly along the time and the frequency axis. Extrinsic information is exchanged between these steps such that increasingly better soft time-frequency masks are obtained, leading to improved speech separation performance in highly reverberant recording conditions."}],"status":"public","oa":"1","date_updated":"2022-01-06T06:51:12Z","author":[{"last_name":"Tran Vu","full_name":"Tran Vu, Dang Hai","first_name":"Dang Hai"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold"}],"date_created":"2019-07-12T05:30:36Z","title":"Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/TrHa2013_01.pdf","open_access":"1"}],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/TrHa2013_01_Presentation.pdf","description":"Presentation","relation":"supplementary_material"}]},"year":"2013","citation":{"ama":"Tran Vu DH, Haeb-Umbach R. Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs. In: <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>. ; 2013.","chicago":"Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs.” In <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","ieee":"D. H. Tran Vu and R. Haeb-Umbach, “Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs,” in <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","apa":"Tran Vu, D. H., &#38; Haeb-Umbach, R. (2013). Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs. In <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>.","bibtex":"@inproceedings{Tran Vu_Haeb-Umbach_2013, title={Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs}, booktitle={21th European Signal Processing Conference (EUSIPCO 2013)}, author={Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2013} }","mla":"Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs.” <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","short":"D.H. Tran Vu, R. Haeb-Umbach, in: 21th European Signal Processing Conference (EUSIPCO 2013), 2013."}},{"publication_identifier":{"issn":["1520-6149"]},"year":"2013","page":"863-867","citation":{"ieee":"D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation,” in <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–867.","chicago":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 863–67, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>.","ama":"Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In: <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:863-867. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>","bibtex":"@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013}, pages={863–867} }","mla":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–67, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>.","short":"D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.","apa":"Vu, D. H. T., &#38; Haeb-Umbach, R. (2013). Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i> (pp. 863–867). <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>"},"date_updated":"2022-01-06T06:51:12Z","date_created":"2019-07-12T05:30:45Z","author":[{"first_name":"Dang Hai Tran","full_name":"Vu, Dang Hai Tran","last_name":"Vu"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"title":"Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation","doi":"10.1109/ICASSP.2013.6637771","publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","type":"conference","abstract":[{"text":"In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm.","lang":"eng"}],"status":"public","_id":"11917","department":[{"_id":"54"}],"user_id":"44006","keyword":["correlation methods","estimation theory","hidden Markov models","iterative methods","probability","spectral analysis","speech processing","2D HMM","SPP estimates","iterative algorithm","posterior probability estimation","spectral correlation","speech presence probability estimation","state-of-the-art SPP estimation algorithm","temporal correlation","turbo principle","two-dimensional hidden Markov model","Correlation","Decoding","Estimation","Iterative decoding","Noise","Speech","Vectors"],"language":[{"iso":"eng"}]},{"abstract":[{"lang":"eng","text":"In this paper we consider the unsupervised word discovery from phonetic input. We employ a word segmentation algorithm which simultaneously develops a lexicon, i.e., the transcription of a word in terms of a phone sequence, learns a n-gram language model describing word and word sequence probabilities, and carries out the segmentation itself. The underlying statistical model is that of a Pitman-Yor process, a concept known from Bayesian non-parametrics, which allows for an a priori unknown and unlimited number of different words. Using a hierarchy of Pitman-Yor processes, language models of different order can be employed and nesting it with another hierarchy of Pitman-Yor processes on the phone level allows for backing off unknown word unigrams by phone m-grams. We present results on a large-vocabulary task, assuming an error-free phone sequence is given. We finish by discussing options how to cope with noisy phone sequences."}],"status":"public","type":"conference","publication":"IEEE International Conference on Robotics and Automation (ICRA 2013)","language":[{"iso":"eng"}],"_id":"11921","user_id":"44006","department":[{"_id":"54"}],"year":"2013","citation":{"bibtex":"@inproceedings{Walter_Haeb-Umbach_Chaudhuri_Raj_2013, title={Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling}, booktitle={IEEE International Conference on Robotics and Automation (ICRA 2013)}, author={Walter, Oliver and Haeb-Umbach, Reinhold and Chaudhuri, Sourish and Raj, Bhiksha}, year={2013} }","mla":"Walter, Oliver, et al. “Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling.” <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>, 2013.","short":"O. Walter, R. Haeb-Umbach, S. Chaudhuri, B. Raj, in: IEEE International Conference on Robotics and Automation (ICRA 2013), 2013.","apa":"Walter, O., Haeb-Umbach, R., Chaudhuri, S., &#38; Raj, B. (2013). Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling. In <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>.","ama":"Walter O, Haeb-Umbach R, Chaudhuri S, Raj B. Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling. In: <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>. ; 2013.","ieee":"O. Walter, R. Haeb-Umbach, S. Chaudhuri, and B. Raj, “Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling,” in <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>, 2013.","chicago":"Walter, Oliver, Reinhold Haeb-Umbach, Sourish Chaudhuri, and Bhiksha Raj. “Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling.” In <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>, 2013."},"related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Poster.pdf"},{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Spotlight.pdf","relation":"supplementary_material","description":"Spotlight"}]},"title":"Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013.pdf"}],"date_updated":"2022-01-06T06:51:12Z","oa":"1","author":[{"first_name":"Oliver","last_name":"Walter","full_name":"Walter, Oliver"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold"},{"full_name":"Chaudhuri, Sourish","last_name":"Chaudhuri","first_name":"Sourish"},{"first_name":"Bhiksha","last_name":"Raj","full_name":"Raj, Bhiksha"}],"date_created":"2019-07-12T05:30:50Z"},{"language":[{"iso":"eng"}],"user_id":"44006","department":[{"_id":"54"}],"_id":"11924","status":"public","type":"conference","publication":"Automatic Speech Recognition and Understanding Workshop (ASRU 2013)","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13.pdf"}],"title":"Hierarchical System for Word Discovery Exploiting DTW-Based Initialization","date_created":"2019-07-12T05:30:53Z","author":[{"last_name":"Walter","full_name":"Walter, Oliver","first_name":"Oliver"},{"first_name":"Timo","full_name":"Korthals, Timo","last_name":"Korthals"},{"full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold"},{"first_name":"Bhiksha","full_name":"Raj, Bhiksha","last_name":"Raj"}],"date_updated":"2022-01-06T06:51:12Z","oa":"1","citation":{"apa":"Walter, O., Korthals, T., Haeb-Umbach, R., &#38; Raj, B. (2013). Hierarchical System for Word Discovery Exploiting DTW-Based Initialization. In <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>.","bibtex":"@inproceedings{Walter_Korthals_Haeb-Umbach_Raj_2013, title={Hierarchical System for Word Discovery Exploiting DTW-Based Initialization}, booktitle={Automatic Speech Recognition and Understanding Workshop (ASRU 2013)}, author={Walter, Oliver and Korthals, Timo and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2013} }","mla":"Walter, Oliver, et al. “Hierarchical System for Word Discovery Exploiting DTW-Based Initialization.” <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>, 2013.","short":"O. Walter, T. Korthals, R. Haeb-Umbach, B. Raj, in: Automatic Speech Recognition and Understanding Workshop (ASRU 2013), 2013.","ama":"Walter O, Korthals T, Haeb-Umbach R, Raj B. Hierarchical System for Word Discovery Exploiting DTW-Based Initialization. In: <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>. ; 2013.","chicago":"Walter, Oliver, Timo Korthals, Reinhold Haeb-Umbach, and Bhiksha Raj. “Hierarchical System for Word Discovery Exploiting DTW-Based Initialization.” In <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>, 2013.","ieee":"O. Walter, T. Korthals, R. Haeb-Umbach, and B. Raj, “Hierarchical System for Word Discovery Exploiting DTW-Based Initialization,” in <i>Automatic Speech Recognition and Understanding Workshop (ASRU 2013)</i>, 2013."},"year":"2013","related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13_Award.pdf","description":"Award","relation":"supplementary_material"},{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13_Poster.pdf","relation":"supplementary_material","description":"Poster"}]}},{"type":"report","status":"public","abstract":[{"text":"In this paper we present a novel initialization method for unsupervised learning of acoustic patterns in recordings of continuous speech. The pattern discovery task is solved by dynamic time warping whose performance we improve by a smart starting point selection. This enables a more accurate discovery of patterns compared to conventional approaches. After graph-based clustering the patterns are employed for training hidden Markov models for an unsupervised speech acquisition. By iterating between model training and decoding in an EM-like framework the word accuracy is continuously improved. On the TIDIGITS corpus we achieve a word error rate of about 13 percent by the proposed unsupervised pattern discovery approach, which neither assumes knowledge of the acoustic units nor of the labels of the training data.","lang":"eng"}],"department":[{"_id":"54"}],"user_id":"44006","_id":"11926","language":[{"iso":"eng"}],"citation":{"ieee":"O. Walter, J. Schmalenstroeer, and R. Haeb-Umbach, <i>A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>. 2013.","chicago":"Walter, Oliver, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. <i>A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>, 2013.","ama":"Walter O, Schmalenstroeer J, Haeb-Umbach R. <i>A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>.; 2013.","bibtex":"@book{Walter_Schmalenstroeer_Haeb-Umbach_2013, title={A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)}, author={Walter, Oliver and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2013} }","mla":"Walter, Oliver, et al. <i>A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>. 2013.","short":"O. Walter, J. Schmalenstroeer, R. Haeb-Umbach, A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01), 2013.","apa":"Walter, O., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2013). <i>A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>."},"year":"2013","date_created":"2019-07-12T05:30:55Z","author":[{"last_name":"Walter","full_name":"Walter, Oliver","first_name":"Oliver"},{"last_name":"Schmalenstroeer","id":"460","full_name":"Schmalenstroeer, Joerg","first_name":"Joerg"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"oa":"1","date_updated":"2022-01-06T06:51:12Z","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaScHa2013.pdf"}],"title":"A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)"},{"publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","abstract":[{"lang":"eng","text":"In this paper we propose an approach to retrieve the absolute geometry of an acoustic sensor network, consisting of spatially distributed microphone arrays, from reverberant speech input. The calibration relies on direction of arrival measurements of the individual arrays. The proposed calibration algorithm is derived from a maximum-likelihood approach employing circular statistics. Since a sensor node consists of a microphone array with known intra-array geometry, we are able to obtain an absolute geometry estimate, including angles and distances. Simulation results demonstrate the effectiveness of the approach."}],"language":[{"iso":"eng"}],"keyword":["Geometry calibration","microphone arrays","position self-calibration"],"quality_controlled":"1","year":"2013","date_created":"2019-07-12T05:29:07Z","title":"DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic","type":"conference","status":"public","department":[{"_id":"54"}],"user_id":"460","_id":"11832","related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/JaScHa13_Presentation.pdf","description":"Presentation","relation":"supplementary_material"}]},"publication_identifier":{"issn":["1520-6149"]},"page":"116-120","citation":{"ama":"Jacob F, Schmalenstroeer J, Haeb-Umbach R. DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic. In: <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:116-120. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637620\">10.1109/ICASSP.2013.6637620</a>","ieee":"F. Jacob, J. Schmalenstroeer, and R. Haeb-Umbach, “DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic,” in <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 116–120, doi: <a href=\"https://doi.org/10.1109/ICASSP.2013.6637620\">10.1109/ICASSP.2013.6637620</a>.","chicago":"Jacob, Florian, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic.” In <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 116–20, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6637620\">https://doi.org/10.1109/ICASSP.2013.6637620</a>.","bibtex":"@inproceedings{Jacob_Schmalenstroeer_Haeb-Umbach_2013, title={DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6637620\">10.1109/ICASSP.2013.6637620</a>}, booktitle={38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}, author={Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2013}, pages={116–120} }","short":"F. Jacob, J. Schmalenstroeer, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 116–120.","mla":"Jacob, Florian, et al. “DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic.” <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 116–20, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637620\">10.1109/ICASSP.2013.6637620</a>.","apa":"Jacob, F., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2013). DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic. <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 116–120. <a href=\"https://doi.org/10.1109/ICASSP.2013.6637620\">https://doi.org/10.1109/ICASSP.2013.6637620</a>"},"author":[{"first_name":"Florian","full_name":"Jacob, Florian","last_name":"Jacob"},{"last_name":"Schmalenstroeer","full_name":"Schmalenstroeer, Joerg","id":"460","first_name":"Joerg"},{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"}],"oa":"1","date_updated":"2023-10-26T08:11:12Z","doi":"10.1109/ICASSP.2013.6637620","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/JacSchHae_ICASSP2013_Rev2.pdf"}]},{"type":"conference","publication":"21th European Signal Processing Conference (EUSIPCO 2013)","abstract":[{"text":"In this paper we present a combined hardware/software approach for synchronizing the sampling clocks of an acoustic sensor network. A first clock frequency offset estimate is obtained by a time stamp exchange protocol with a low data rate and computational requirements. The estimate is then postprocessed by a Kalman filter which exploits the specific properties of the statistics of the frequency offset estimation error. In long term experiments the deviation between the sampling oscillators of two sensor nodes never exceeded half a sample with a wired and with a wireless link between the nodes. The achieved precision enables the estimation of time difference of arrival values across different hardware devices without sharing a common sampling hardware.","lang":"eng"}],"status":"public","_id":"11891","user_id":"460","department":[{"_id":"54"}],"keyword":["synchronization","acoustic sensor network"],"language":[{"iso":"eng"}],"quality_controlled":"1","related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/SchHaeb2013_Presentation.pdf","description":"Presentation","relation":"supplementary_material"}]},"year":"2013","citation":{"ama":"Schmalenstroeer J, Haeb-Umbach R. Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model. In: <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>. ; 2013.","chicago":"Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model.” In <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","ieee":"J. Schmalenstroeer and R. Haeb-Umbach, “Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model,” 2013.","apa":"Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2013). Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model. <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>.","short":"J. Schmalenstroeer, R. Haeb-Umbach, in: 21th European Signal Processing Conference (EUSIPCO 2013), 2013.","bibtex":"@inproceedings{Schmalenstroeer_Haeb-Umbach_2013, title={Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model}, booktitle={21th European Signal Processing Conference (EUSIPCO 2013)}, author={Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2013} }","mla":"Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model.” <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013."},"oa":"1","date_updated":"2023-10-26T08:11:01Z","author":[{"id":"460","full_name":"Schmalenstroeer, Joerg","last_name":"Schmalenstroeer","first_name":"Joerg"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"date_created":"2019-07-12T05:30:15Z","title":"Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/SchHaeb2013.pdf","open_access":"1"}]},{"publication":"Positioning Navigation and Communication (WPNC), 2013 10th Workshop on","type":"conference","status":"public","abstract":[{"lang":"eng","text":"In this paper we present a system for indoor navigation based on received signal strength index information of Wireless-LAN access points and relative position estimates. The relative position information is gathered from inertial smartphone sensors using a step detection and an orientation estimate. Our map data is hosted on a server employing a map renderer and a SQL database. The database includes a complete multilevel office building, within which the user can navigate. During navigation, the client retrieves the position estimate from the server, together with the corresponding map tiles to visualize the user's position on the smartphone display."}],"department":[{"_id":"54"}],"user_id":"460","_id":"11818","language":[{"iso":"eng"}],"keyword":["SQL","navigation","smart phones","wireless LAN","RSSI","SQL database","complete multilevel office building","inertial sensor information","inertial smartphone sensors","map renderer","received signal strength index information","relative position estimates","server based indoor navigation","step detection","wireless-LAN access points","Smartphone","fingerprint","indoor navigation","map tile"],"related_material":{"link":[{"description":"Poster","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrScHa2013_Poster.pdf"}]},"quality_controlled":"1","page":"1-6","citation":{"chicago":"Hoang, Manh Kha, Sarah Schmitz, Christian Drueke, Dang Hai Tran Vu, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Server Based Indoor Navigation Using RSSI and Inertial Sensor Information.” In <i>Positioning Navigation and Communication (WPNC), 2013 10th Workshop On</i>, 1–6, 2013. <a href=\"https://doi.org/10.1109/WPNC.2013.6533263\">https://doi.org/10.1109/WPNC.2013.6533263</a>.","ieee":"M. K. Hoang, S. Schmitz, C. Drueke, D. H. T. Vu, J. Schmalenstroeer, and R. Haeb-Umbach, “Server based indoor navigation using RSSI and inertial sensor information,” in <i>Positioning Navigation and Communication (WPNC), 2013 10th Workshop on</i>, 2013, pp. 1–6, doi: <a href=\"https://doi.org/10.1109/WPNC.2013.6533263\">10.1109/WPNC.2013.6533263</a>.","ama":"Hoang MK, Schmitz S, Drueke C, Vu DHT, Schmalenstroeer J, Haeb-Umbach R. Server based indoor navigation using RSSI and inertial sensor information. In: <i>Positioning Navigation and Communication (WPNC), 2013 10th Workshop On</i>. ; 2013:1-6. doi:<a href=\"https://doi.org/10.1109/WPNC.2013.6533263\">10.1109/WPNC.2013.6533263</a>","short":"M.K. Hoang, S. Schmitz, C. Drueke, D.H.T. Vu, J. Schmalenstroeer, R. Haeb-Umbach, in: Positioning Navigation and Communication (WPNC), 2013 10th Workshop On, 2013, pp. 1–6.","mla":"Hoang, Manh Kha, et al. “Server Based Indoor Navigation Using RSSI and Inertial Sensor Information.” <i>Positioning Navigation and Communication (WPNC), 2013 10th Workshop On</i>, 2013, pp. 1–6, doi:<a href=\"https://doi.org/10.1109/WPNC.2013.6533263\">10.1109/WPNC.2013.6533263</a>.","bibtex":"@inproceedings{Hoang_Schmitz_Drueke_Vu_Schmalenstroeer_Haeb-Umbach_2013, title={Server based indoor navigation using RSSI and inertial sensor information}, DOI={<a href=\"https://doi.org/10.1109/WPNC.2013.6533263\">10.1109/WPNC.2013.6533263</a>}, booktitle={Positioning Navigation and Communication (WPNC), 2013 10th Workshop on}, author={Hoang, Manh Kha and Schmitz, Sarah and Drueke, Christian and Vu, Dang Hai Tran and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2013}, pages={1–6} }","apa":"Hoang, M. K., Schmitz, S., Drueke, C., Vu, D. H. T., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2013). Server based indoor navigation using RSSI and inertial sensor information. <i>Positioning Navigation and Communication (WPNC), 2013 10th Workshop On</i>, 1–6. <a href=\"https://doi.org/10.1109/WPNC.2013.6533263\">https://doi.org/10.1109/WPNC.2013.6533263</a>"},"year":"2013","author":[{"full_name":"Hoang, Manh Kha","last_name":"Hoang","first_name":"Manh Kha"},{"first_name":"Sarah","last_name":"Schmitz","full_name":"Schmitz, Sarah"},{"first_name":"Christian","full_name":"Drueke, Christian","last_name":"Drueke"},{"first_name":"Dang Hai Tran","last_name":"Vu","full_name":"Vu, Dang Hai Tran"},{"first_name":"Joerg","last_name":"Schmalenstroeer","id":"460","full_name":"Schmalenstroeer, Joerg"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"date_created":"2019-07-12T05:28:51Z","date_updated":"2023-10-26T08:09:36Z","oa":"1","doi":"10.1109/WPNC.2013.6533263","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrScHa2013.pdf"}],"title":"Server based indoor navigation using RSSI and inertial sensor information"},{"_id":"11817","department":[{"_id":"54"}],"user_id":"460","language":[{"iso":"eng"}],"publication":"21th European Signal Processing Conference (EUSIPCO 2013)","type":"conference","abstract":[{"text":"In this paper we present a modified hidden Markov model (HMM) for the fusion of received signal strength index (RSSI) information of WiFi access points and relative position information which is obtained from the inertial sensors of a smartphone for indoor positioning. Since the states of the HMM represent the potential user locations, their number determines the quantization error introduced by discretizing the allowable user positions through the use of the HMM. To reduce this quantization error we introduce â??pseudoâ?? states, whose emission probability, which models the RSSI measurements at this location, is synthesized from those of the neighboring states of which a Gaussian emission probability has been estimated during the training phase. The experimental results demonstrate the effectiveness of this approach. By introducing on average two pseudo states per original HMM state the positioning error could be significantly reduced without increasing the training effort.","lang":"eng"}],"status":"public","oa":"1","date_updated":"2023-10-26T08:09:45Z","date_created":"2019-07-12T05:28:50Z","author":[{"first_name":"Manh Kha","full_name":"Hoang, Manh Kha","last_name":"Hoang"},{"id":"460","full_name":"Schmalenstroeer, Joerg","last_name":"Schmalenstroeer","first_name":"Joerg"},{"full_name":"Drueke, Christian","last_name":"Drueke","first_name":"Christian"},{"first_name":"Dang Hai","full_name":"Tran Vu, Dang Hai","last_name":"Tran Vu"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"title":"A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrHa2013.pdf","open_access":"1"}],"quality_controlled":"1","related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrHa2013_Poster.pdf","description":"Poster","relation":"supplementary_material"}]},"year":"2013","citation":{"apa":"Hoang, M. K., Schmalenstroeer, J., Drueke, C., Tran Vu, D. H., &#38; Haeb-Umbach, R. (2013). A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection. <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>.","short":"M.K. Hoang, J. Schmalenstroeer, C. Drueke, D.H. Tran Vu, R. Haeb-Umbach, in: 21th European Signal Processing Conference (EUSIPCO 2013), 2013.","bibtex":"@inproceedings{Hoang_Schmalenstroeer_Drueke_Tran Vu_Haeb-Umbach_2013, title={A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection}, booktitle={21th European Signal Processing Conference (EUSIPCO 2013)}, author={Hoang, Manh Kha and Schmalenstroeer, Joerg and Drueke, Christian and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2013} }","mla":"Hoang, Manh Kha, et al. “A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection.” <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","ieee":"M. K. Hoang, J. Schmalenstroeer, C. Drueke, D. H. Tran Vu, and R. Haeb-Umbach, “A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection,” 2013.","chicago":"Hoang, Manh Kha, Joerg Schmalenstroeer, Christian Drueke, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection.” In <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.","ama":"Hoang MK, Schmalenstroeer J, Drueke C, Tran Vu DH, Haeb-Umbach R. A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection. In: <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>. ; 2013."}},{"user_id":"44006","department":[{"_id":"54"}],"_id":"11741","language":[{"iso":"eng"}],"type":"conference","publication":"Speech Communication; 10. ITG Symposium; Proceedings.","status":"public","date_created":"2019-07-12T05:27:22Z","author":[{"first_name":"Aleksej","last_name":"Chinaev","full_name":"Chinaev, Aleksej"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_updated":"2022-01-06T06:51:08Z","oa":"1","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/ChHa12.pdf","open_access":"1"}],"title":"Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density Tracker","related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2012/ChHa12_Poster.pdf"}]},"citation":{"apa":"Chinaev, A., &#38; Haeb-Umbach, R. (2012). Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density Tracker. In <i>Speech Communication; 10. ITG Symposium; Proceedings.</i>","short":"A. Chinaev, R. Haeb-Umbach, in: Speech Communication; 10. ITG Symposium; Proceedings., 2012.","mla":"Chinaev, Aleksej, and Reinhold Haeb-Umbach. “Quality Analysis and Optimization of the MAP-Based Noise Power Spectral Density Tracker.” <i>Speech Communication; 10. ITG Symposium; Proceedings.</i>, 2012.","bibtex":"@inproceedings{Chinaev_Haeb-Umbach_2012, title={Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density Tracker}, booktitle={Speech Communication; 10. ITG Symposium; Proceedings.}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2012} }","chicago":"Chinaev, Aleksej, and Reinhold Haeb-Umbach. “Quality Analysis and Optimization of the MAP-Based Noise Power Spectral Density Tracker.” In <i>Speech Communication; 10. ITG Symposium; Proceedings.</i>, 2012.","ieee":"A. Chinaev and R. Haeb-Umbach, “Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density Tracker,” in <i>Speech Communication; 10. ITG Symposium; Proceedings.</i>, 2012.","ama":"Chinaev A, Haeb-Umbach R. Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density Tracker. In: <i>Speech Communication; 10. ITG Symposium; Proceedings.</i> ; 2012."},"year":"2012"},{"_id":"11745","department":[{"_id":"54"}],"user_id":"44006","keyword":["MAP parameter estimation","noise power estimation","speech enhancement"],"language":[{"iso":"eng"}],"publication":"37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)","type":"conference","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","oa":"1","date_updated":"2022-01-06T06:51:08Z","author":[{"first_name":"Aleksej","full_name":"Chinaev, Aleksej","last_name":"Chinaev"},{"first_name":"Alexander","last_name":"Krueger","full_name":"Krueger, Alexander"},{"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_created":"2019-07-12T05:27:26Z","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"}],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12_Talk.pdf","relation":"supplementary_material","description":"Presentation"}]},"year":"2012","citation":{"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} }","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.","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>.","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."}},{"citation":{"ama":"Krueger A, Haeb-Umbach R. Reverberant Speech Recognition. In: <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>. Wiley; 2012.","chicago":"Krueger, Alexander, and Reinhold Haeb-Umbach. “Reverberant Speech Recognition.” In <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>. Wiley, 2012.","ieee":"A. Krueger and R. Haeb-Umbach, “Reverberant Speech Recognition,” in <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>, Wiley, 2012.","bibtex":"@inbook{Krueger_Haeb-Umbach_2012, title={Reverberant Speech Recognition}, booktitle={Techniques for Noise Robustness in Automatic Speech Recognition}, publisher={Wiley}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2012} }","mla":"Krueger, Alexander, and Reinhold Haeb-Umbach. “Reverberant Speech Recognition.” <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>, Wiley, 2012.","short":"A. Krueger, R. Haeb-Umbach, in: Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, 2012.","apa":"Krueger, A., &#38; Haeb-Umbach, R. (2012). Reverberant Speech Recognition. In <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>. Wiley."},"year":"2012","author":[{"full_name":"Krueger, Alexander","last_name":"Krueger","first_name":"Alexander"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold"}],"date_created":"2019-07-12T05:29:21Z","date_updated":"2022-01-06T06:51:11Z","publisher":"Wiley","title":"Reverberant Speech Recognition","type":"book_chapter","publication":"Techniques for Noise Robustness in Automatic Speech Recognition","status":"public","user_id":"44006","department":[{"_id":"54"}],"_id":"11844","language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"_id":"11849","department":[{"_id":"54"}],"user_id":"44006","abstract":[{"text":"In this contribution we investigate the effectiveness of Bayesian feature enhancement (BFE) on a medium-sized recognition task containing real-world recordings of noisy reverberant speech. BFE employs a very coarse model of the acoustic impulse response (AIR) from the source to the microphone, which has been shown to be effective if the speech to be recognized has been generated by artificially convolving nonreverberant speech with a constant AIR. Here we demonstrate that the model is also appropriate to be used in feature enhancement of true recordings of noisy reverberant speech. On the Multi-Channel Wall Street Journal Audio Visual corpus (MC-WSJ-AV) the word error rate is cut in half to 41.9 percent compared to the ETSI Standard Front-End using as input the signal of a single distant microphone with a single recognition pass.","lang":"eng"}],"status":"public","publication":"Proc. Interspeech","type":"conference","title":"Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/KrWaLeHa2012.pdf","open_access":"1"}],"date_updated":"2022-01-06T06:51:11Z","oa":"1","date_created":"2019-07-12T05:29:27Z","author":[{"first_name":"Alexander","last_name":"Krueger","full_name":"Krueger, Alexander"},{"first_name":"Oliver","full_name":"Walter, Oliver","last_name":"Walter"},{"first_name":"Volker","full_name":"Leutnant, Volker","last_name":"Leutnant"},{"full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"place":"Portland, USA","year":"2012","citation":{"ama":"Krueger A, Walter O, Leutnant V, Haeb-Umbach R. Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data. In: <i>Proc. Interspeech</i>. Portland, USA; 2012.","ieee":"A. Krueger, O. Walter, V. Leutnant, and R. Haeb-Umbach, “Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data,” in <i>Proc. Interspeech</i>, 2012.","chicago":"Krueger, Alexander, Oliver Walter, Volker Leutnant, and Reinhold Haeb-Umbach. “Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data.” In <i>Proc. Interspeech</i>. Portland, USA, 2012.","apa":"Krueger, A., Walter, O., Leutnant, V., &#38; Haeb-Umbach, R. (2012). Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data. In <i>Proc. Interspeech</i>. Portland, USA.","short":"A. Krueger, O. Walter, V. Leutnant, R. Haeb-Umbach, in: Proc. Interspeech, Portland, USA, 2012.","bibtex":"@inproceedings{Krueger_Walter_Leutnant_Haeb-Umbach_2012, place={Portland, USA}, title={Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data}, booktitle={Proc. Interspeech}, author={Krueger, Alexander and Walter, Oliver and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2012} }","mla":"Krueger, Alexander, et al. “Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data.” <i>Proc. Interspeech</i>, 2012."}},{"abstract":[{"lang":"eng","text":"In this contribution, a new observation model for the joint compensation of reverberation and noise in the logarithmic mel power spectral density domain will be considered. The proposed observation model relates the noisy reverberant feature to the underlying sequence of clean speech features and the feature of the noise. Nevertheless, due to the complex interaction of these variables in the target domain, the observationmodel cannot be applied to Bayesian feature enhancement directly, calling for approximations that eventually render the observation model useful. The performance of the approximated observation model will highly depend on the capability of modeling the difference between the model and the noisy reverberant observation. A detailed analysis of this observation error will be provided in this work. Among others, it will point out the need to account for the instantaneous ratio of the reverberant speech power and the noise power. Index Terms: Bayesian feature enhancement, observation model for noisy reverberant speech"}],"status":"public","publication":"Speech Communication; 10. ITG Symposium; Proceedings of","type":"journal_article","language":[{"iso":"eng"}],"_id":"11863","department":[{"_id":"54"}],"user_id":"44006","year":"2012","page":"1-4","citation":{"ama":"Leutnant V, Krueger A, Haeb-Umbach R. Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech. <i>Speech Communication; 10 ITG Symposium; Proceedings of</i>. 2012:1-4.","chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech.” <i>Speech Communication; 10. ITG Symposium; Proceedings Of</i>, 2012, 1–4.","ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech,” <i>Speech Communication; 10. ITG Symposium; Proceedings of</i>, pp. 1–4, 2012.","mla":"Leutnant, Volker, et al. “Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech.” <i>Speech Communication; 10. ITG Symposium; Proceedings Of</i>, 2012, pp. 1–4.","bibtex":"@article{Leutnant_Krueger_Haeb-Umbach_2012, title={Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech}, journal={Speech Communication; 10. ITG Symposium; Proceedings of}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2012}, pages={1–4} }","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, Speech Communication; 10. ITG Symposium; Proceedings Of (2012) 1–4.","apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2012). Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech. <i>Speech Communication; 10. ITG Symposium; Proceedings Of</i>, 1–4."},"title":"Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech","main_file_link":[{"open_access":"1","url":"http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6309628"}],"date_updated":"2022-01-06T06:51:11Z","oa":"1","author":[{"full_name":"Leutnant, Volker","last_name":"Leutnant","first_name":"Volker"},{"last_name":"Krueger","full_name":"Krueger, Alexander","first_name":"Alexander"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_created":"2019-07-12T05:29:43Z"},{"date_created":"2019-07-12T05:29:44Z","author":[{"last_name":"Leutnant","full_name":"Leutnant, Volker","first_name":"Volker"},{"first_name":"Alexander","full_name":"Krueger, Alexander","last_name":"Krueger"},{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"}],"oa":"1","date_updated":"2022-01-06T06:51:11Z","main_file_link":[{"open_access":"1","url":"http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6335731"}],"title":"A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR","citation":{"ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR,” in <i>Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference on</i>, 2012.","chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A Statistical Observation Model For Noisy Reverberant Speech Features and Its Application to Robust ASR.” In <i>Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference On</i>, 2012.","ama":"Leutnant V, Krueger A, Haeb-Umbach R. A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR. In: <i>Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference On</i>. ; 2012.","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, in: Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference On, 2012.","mla":"Leutnant, Volker, et al. “A Statistical Observation Model For Noisy Reverberant Speech Features and Its Application to Robust ASR.” <i>Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference On</i>, 2012.","bibtex":"@inproceedings{Leutnant_Krueger_Haeb-Umbach_2012, title={A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR}, booktitle={Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference on}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2012} }","apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2012). A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR. In <i>Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference on</i>."},"year":"2012","department":[{"_id":"54"}],"user_id":"44006","_id":"11864","language":[{"iso":"eng"}],"keyword":["Robust Automatic Speech Recognition","Bayesian feature enhancement","observation model for reverberant and noisy speech"],"publication":"Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference on","type":"conference","status":"public","abstract":[{"text":"In this work, an observation model for the joint compensation of noise and reverberation in the logarithmic mel power spectral density domain is considered. It relates the features of the noisy reverberant speech to those of the non-reverberant speech and the noise. In contrast to enhancement of features only corrupted by reverberation (reverberant features), enhancement of noisy reverberant features requires a more sophisticated model for the error introduced by the proposed observation model. In a first consideration, it will be shown that this error is highly dependent on the instantaneous ratio of the power of reverberant speech to the power of the noise and, moreover, sensitive to the phase between reverberant speech and noise in the short-time discrete Fourier domain. Afterwards, a statistically motivated approach will be presented allowing for the model of the observation error to be inferred from the error model previously used for the reverberation only case. Finally, the developed observation error model will be utilized in a Bayesian feature enhancement scheme, leading to improvements in word accuracy on the AURORA5 database.","lang":"eng"}]},{"type":"report","status":"public","user_id":"44006","department":[{"_id":"54"}],"_id":"11865","language":[{"iso":"eng"}],"citation":{"chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. <i>Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain</i>, 2012.","ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, <i>Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain</i>. 2012.","ama":"Leutnant V, Krueger A, Haeb-Umbach R. <i>Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain</i>.; 2012.","apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2012). <i>Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain</i>.","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain, 2012.","bibtex":"@book{Leutnant_Krueger_Haeb-Umbach_2012, title={Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2012} }","mla":"Leutnant, Volker, et al. <i>Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain</i>. 2012."},"year":"2012","date_created":"2019-07-12T05:29:45Z","author":[{"first_name":"Volker","full_name":"Leutnant, Volker","last_name":"Leutnant"},{"last_name":"Krueger","full_name":"Krueger, Alexander","first_name":"Alexander"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_updated":"2022-01-06T06:51:11Z","oa":"1","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/LeuKruHab2012c.pdf","open_access":"1"}],"title":"Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain"},{"date_created":"2019-07-12T05:30:37Z","author":[{"last_name":"Tran Vu","full_name":"Tran Vu, Dang Hai","first_name":"Dang Hai"},{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"}],"date_updated":"2022-01-06T06:51:12Z","title":"Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models","citation":{"bibtex":"@inproceedings{Tran Vu_Haeb-Umbach_2012, title={Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models}, booktitle={International Workshop on Acoustic Signal Enhancement (IWAENC2012)}, author={Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2012} }","short":"D.H. Tran Vu, R. Haeb-Umbach, in: International Workshop on Acoustic Signal Enhancement (IWAENC2012), 2012.","mla":"Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression Using Hidden Markov Models.” <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>, 2012.","apa":"Tran Vu, D. H., &#38; Haeb-Umbach, R. (2012). Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models. In <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>.","ieee":"D. H. Tran Vu and R. Haeb-Umbach, “Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models,” in <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>, 2012.","chicago":"Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression Using Hidden Markov Models.” In <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>, 2012.","ama":"Tran Vu DH, Haeb-Umbach R. Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models. In: <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>. ; 2012."},"year":"2012","department":[{"_id":"54"}],"user_id":"44006","_id":"11910","language":[{"iso":"eng"}],"publication":"International Workshop on Acoustic Signal Enhancement (IWAENC2012)","type":"conference","status":"public"},{"_id":"11833","department":[{"_id":"54"}],"user_id":"460","keyword":["Unsupervised","geometry calibration","microphone arrays","position self-calibration"],"language":[{"iso":"eng"}],"publication":"International Workshop on Acoustic Signal Enhancement (IWAENC 2012)","type":"conference","abstract":[{"text":"In this paper we propose an approach to retrieve the geometry of an acoustic sensor network consisting of spatially distributed microphone arrays from unconstrained speech input. The calibration relies on Direction of Arrival (DoA) measurements which do not require a clock synchronization among the sensor nodes. The calibration problem is formulated as a cost function optimization task, which minimizes the squared differences between measured and predicted observations and additionally avoids the existence of minima that correspond to mirrored versions of the actual sensor orientations. Further, outlier measurements caused by reverberation are mitigated by a Random Sample Consensus (RANSAC) approach. The experimental results show a mean positioning error of at most 25 cm even in highly reverberant environments.","lang":"eng"}],"status":"public","date_updated":"2023-10-26T08:10:52Z","oa":"1","author":[{"last_name":"Jacob","full_name":"Jacob, Florian","first_name":"Florian"},{"first_name":"Joerg","full_name":"Schmalenstroeer, Joerg","id":"460","last_name":"Schmalenstroeer"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"date_created":"2019-07-12T05:29:08Z","title":"Microphone Array Position Self-Calibration from Reverberant Speech Input","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12.pdf"}],"quality_controlled":"1","related_material":{"link":[{"relation":"supplementary_material","description":"Video","url":"https://groups.uni-paderborn.de/nt/pubs/2012/Microphine_Array_Position_Self-Calibration_from_Reverberant_Speech_Input.mp4"},{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12_Poster.pdf","description":"Poster","relation":"supplementary_material"},{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12_Demonstrator.pdf","description":"Demonstrator","relation":"supplementary_material"}]},"year":"2012","citation":{"ama":"Jacob F, Schmalenstroeer J, Haeb-Umbach R. Microphone Array Position Self-Calibration from Reverberant Speech Input. In: <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>. ; 2012.","chicago":"Jacob, Florian, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Microphone Array Position Self-Calibration from Reverberant Speech Input.” In <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>, 2012.","ieee":"F. Jacob, J. Schmalenstroeer, and R. Haeb-Umbach, “Microphone Array Position Self-Calibration from Reverberant Speech Input,” 2012.","apa":"Jacob, F., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2012). Microphone Array Position Self-Calibration from Reverberant Speech Input. <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>.","short":"F. Jacob, J. Schmalenstroeer, R. Haeb-Umbach, in: International Workshop on Acoustic Signal Enhancement (IWAENC 2012), 2012.","bibtex":"@inproceedings{Jacob_Schmalenstroeer_Haeb-Umbach_2012, title={Microphone Array Position Self-Calibration from Reverberant Speech Input}, booktitle={International Workshop on Acoustic Signal Enhancement (IWAENC 2012)}, author={Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2012} }","mla":"Jacob, Florian, et al. “Microphone Array Position Self-Calibration from Reverberant Speech Input.” <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>, 2012."}}]