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Universität Paderborn; 2016."},"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Subproject A3","_id":"7"},{"name":"SFB 901 - Project Area A","_id":"2"}],"date_created":"2017-12-19T10:10:17Z","type":"bachelorsthesis","department":[{"_id":"63"},{"_id":"541"}]},{"user_id":"44006","language":[{"iso":"eng"}],"_id":"11738","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2016/ChHa16.pdf"}],"date_updated":"2022-01-06T06:51:08Z","author":[{"last_name":"Chinaev","first_name":"Aleksej","full_name":"Chinaev, Aleksej"},{"last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"status":"public","title":"A Priori SNR Estimation Using a Generalized Decision Directed Approach","year":"2016","department":[{"_id":"54"}],"oa":"1","type":"conference","date_created":"2019-07-12T05:27:18Z","abstract":[{"lang":"eng","text":"In this contribution we investigate a priori signal-to-noise ratio (SNR) estimation, a crucial component of a single-channel speech enhancement system based on spectral subtraction. The majority of the state-of-the art a priori SNR estimators work in the power spectral domain, which is, however, not confirmed to be the optimal domain for the estimation. Motivated by the generalized spectral subtraction rule, we show how the estimation of the a priori SNR can be formulated in the so called generalized SNR domain. This formulation allows to generalize the widely used decision directed (DD) approach. An experimental investigation with different noise types reveals the superiority of the generalized DD approach over the conventional DD approach in terms of both the mean opinion score - listening quality objective measure and the output global SNR in the medium to high input SNR regime, while we show that the power spectrum is the optimal domain for low SNR. We further develop a parameterization which adjusts the domain of estimation automatically according to the estimated input global SNR. Index Terms: single-channel speech enhancement, a priori SNR estimation, generalized spectral subtraction"}],"related_material":{"link":[{"description":"Poster","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2016/ChHa16_Poster.pdf"}]},"citation":{"bibtex":"@inproceedings{Chinaev_Haeb-Umbach_2016, title={A Priori SNR Estimation Using a Generalized Decision Directed Approach}, booktitle={INTERSPEECH 2016, San Francisco, USA}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2016} }","ama":"Chinaev A, Haeb-Umbach R. A Priori SNR Estimation Using a Generalized Decision Directed Approach. In: <i>INTERSPEECH 2016, San Francisco, USA</i>. ; 2016.","mla":"Chinaev, Aleksej, and Reinhold Haeb-Umbach. “A Priori SNR Estimation Using a Generalized Decision Directed Approach.” <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016.","short":"A. Chinaev, R. Haeb-Umbach, in: INTERSPEECH 2016, San Francisco, USA, 2016.","chicago":"Chinaev, Aleksej, and Reinhold Haeb-Umbach. “A Priori SNR Estimation Using a Generalized Decision Directed Approach.” In <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016.","ieee":"A. Chinaev and R. Haeb-Umbach, “A Priori SNR Estimation Using a Generalized Decision Directed Approach,” in <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016.","apa":"Chinaev, A., &#38; Haeb-Umbach, R. (2016). A Priori SNR Estimation Using a Generalized Decision Directed Approach. In <i>INTERSPEECH 2016, San Francisco, USA</i>."},"publication":"INTERSPEECH 2016, San Francisco, USA"},{"department":[{"_id":"54"}],"oa":"1","type":"conference","date_created":"2019-07-12T05:27:24Z","related_material":{"link":[{"relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2016/ChHeiHa16_Presentation.pdf","description":"Presentation"}]},"abstract":[{"text":"This contribution introduces a novel causal a priori signal-to-noise ratio (SNR) estimator for single-channel speech enhancement. To exploit the advantages of the generalized spectral subtraction, a normalized ?-order magnitude (NAOM) domain is introduced where an a priori SNR estimation is carried out. In this domain, the NAOM coefficients of noise and clean speech signals are modeled by a Weibull distribution and aWeibullmixturemodel (WMM), respectively. While the parameters of the noise model are calculated from the noise power spectral density estimates, the speechWMM parameters are estimated from the noisy signal by applying a causal Expectation-Maximization algorithm. Further a maximum a posteriori estimate of the a priori SNR is developed. The experiments in different noisy environments show the superiority of the proposed estimator compared to the well-known decision-directed approach in terms of estimation error, estimator variance and speech quality of the enhanced signals when used for speech enhancement.","lang":"eng"}],"citation":{"apa":"Chinaev, A., Heitkaemper, J., &#38; Haeb-Umbach, R. (2016). A Priori SNR Estimation Using Weibull Mixture Model. In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>.","ieee":"A. Chinaev, J. Heitkaemper, and R. Haeb-Umbach, “A Priori SNR Estimation Using Weibull Mixture Model,” in <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","chicago":"Chinaev, Aleksej, Jens Heitkaemper, and Reinhold Haeb-Umbach. “A Priori SNR Estimation Using Weibull Mixture Model.” In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","short":"A. Chinaev, J. Heitkaemper, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.","mla":"Chinaev, Aleksej, et al. “A Priori SNR Estimation Using Weibull Mixture Model.” <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","ama":"Chinaev A, Heitkaemper J, Haeb-Umbach R. A Priori SNR Estimation Using Weibull Mixture Model. In: <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>. ; 2016.","bibtex":"@inproceedings{Chinaev_Heitkaemper_Haeb-Umbach_2016, title={A Priori SNR Estimation Using Weibull Mixture Model}, booktitle={12. ITG Fachtagung Sprachkommunikation (ITG 2016)}, author={Chinaev, Aleksej and Heitkaemper, Jens and Haeb-Umbach, Reinhold}, year={2016} }"},"publication":"12. ITG Fachtagung Sprachkommunikation (ITG 2016)","user_id":"44006","language":[{"iso":"eng"}],"_id":"11743","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2016/ChHeiHa16.pdf"}],"date_updated":"2022-01-06T06:51:08Z","author":[{"full_name":"Chinaev, Aleksej","last_name":"Chinaev","first_name":"Aleksej"},{"full_name":"Heitkaemper, Jens","last_name":"Heitkaemper","first_name":"Jens","id":"27643"},{"id":"242","first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold"}],"title":"A Priori SNR Estimation Using Weibull Mixture Model","status":"public","year":"2016"},{"publication":"12. ITG Fachtagung Sprachkommunikation (ITG 2016)","citation":{"ieee":"A. Chinaev, J. Heymann, L. Drude, and R. Haeb-Umbach, “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs,” in <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","apa":"Chinaev, A., Heymann, J., Drude, L., &#38; Haeb-Umbach, R. (2016). Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs. In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>.","chicago":"Chinaev, Aleksej, Jahn Heymann, Lukas Drude, and Reinhold Haeb-Umbach. “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","short":"A. Chinaev, J. Heymann, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.","mla":"Chinaev, Aleksej, et al. “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","bibtex":"@inproceedings{Chinaev_Heymann_Drude_Haeb-Umbach_2016, title={Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs}, booktitle={12. ITG Fachtagung Sprachkommunikation (ITG 2016)}, author={Chinaev, Aleksej and Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016} }","ama":"Chinaev A, Heymann J, Drude L, Haeb-Umbach R. Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs. In: <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>. ; 2016."},"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/ChHeyDrHa16_Presentation.pdf","relation":"supplementary_material","description":"Presentation"}]},"abstract":[{"lang":"eng","text":"A noise power spectral density (PSD) estimation is an indispensable component of speech spectral enhancement systems. In this paper we present a noise PSD tracking algorithm, which employs a noise presence probability estimate delivered by a deep neural network (DNN). The algorithm provides a causal noise PSD estimate and can thus be used in speech enhancement systems for communication purposes. An extensive performance comparison has been carried out with ten causal state-of-the-art noise tracking algorithms taken from the literature and categorized acc. to applied techniques. The experiments showed that the proposed DNN-based noise PSD tracker outperforms all competing methods with respect to all tested performance measures, which include the noise tracking performance and the performance of a speech enhancement system employing the noise tracking component."}],"date_created":"2019-07-12T05:27:25Z","type":"conference","department":[{"_id":"54"}],"oa":"1","title":"Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs","status":"public","year":"2016","author":[{"full_name":"Chinaev, Aleksej","first_name":"Aleksej","last_name":"Chinaev"},{"id":"9168","first_name":"Jahn","last_name":"Heymann","full_name":"Heymann, Jahn"},{"first_name":"Lukas","last_name":"Drude","full_name":"Drude, Lukas","id":"11213"},{"full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242"}],"date_updated":"2022-01-06T06:51:08Z","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2016/ChHeyDrHa16.pdf"}],"_id":"11744","language":[{"iso":"eng"}],"user_id":"44006"},{"date_updated":"2022-01-06T06:51:08Z","author":[{"id":"11213","full_name":"Drude, Lukas","first_name":"Lukas","last_name":"Drude"},{"full_name":"Boeddeker, Christoph","first_name":"Christoph","last_name":"Boeddeker","id":"40767"},{"full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242"}],"status":"public","year":"2016","title":"Blind Speech Separation based on Complex Spherical k-Mode Clustering","user_id":"44006","language":[{"iso":"eng"}],"_id":"11751","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_drude_paper.pdf"}],"related_material":{"link":[{"relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_drude_slides.pdf","description":"Slides"}]},"citation":{"mla":"Drude, Lukas, et al. “Blind Speech Separation Based on Complex Spherical K-Mode Clustering.” <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016.","ama":"Drude L, Boeddeker C, Haeb-Umbach R. Blind Speech Separation based on Complex Spherical k-Mode Clustering. In: <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>. ; 2016.","bibtex":"@inproceedings{Drude_Boeddeker_Haeb-Umbach_2016, title={Blind Speech Separation based on Complex Spherical k-Mode Clustering}, booktitle={Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Drude, Lukas and Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2016} }","apa":"Drude, L., Boeddeker, C., &#38; Haeb-Umbach, R. (2016). Blind Speech Separation based on Complex Spherical k-Mode Clustering. In <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>.","ieee":"L. Drude, C. Boeddeker, and R. Haeb-Umbach, “Blind Speech Separation based on Complex Spherical k-Mode Clustering,” in <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016.","chicago":"Drude, Lukas, Christoph Boeddeker, and Reinhold Haeb-Umbach. “Blind Speech Separation Based on Complex Spherical K-Mode Clustering.” In <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016.","short":"L. Drude, C. Boeddeker, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016."},"publication":"Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)","department":[{"_id":"54"}],"oa":"1","type":"conference","date_created":"2019-07-12T05:27:33Z"},{"user_id":"44006","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/interspeech_2016_drude_paper.pdf","open_access":"1"}],"language":[{"iso":"eng"}],"_id":"11756","date_updated":"2022-01-06T06:51:08Z","status":"public","year":"2016","title":"On the appropriateness of complex-valued neural networks for speech enhancement","author":[{"id":"11213","first_name":"Lukas","last_name":"Drude","full_name":"Drude, Lukas"},{"first_name":"Bhiksha","last_name":"Raj","full_name":"Raj, Bhiksha"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"type":"conference","oa":"1","department":[{"_id":"54"}],"date_created":"2019-07-12T05:27:39Z","abstract":[{"text":"Although complex-valued neural networks (CVNNs) â?? networks which can operate with complex arithmetic â?? have been around for a while, they have not been given reconsideration since the breakthrough of deep network architectures. This paper presents a critical assessment whether the novel tool set of deep neural networks (DNNs) should be extended to complex-valued arithmetic. Indeed, with DNNs making inroads in speech enhancement tasks, the use of complex-valued input data, specifically the short-time Fourier transform coefficients, is an obvious consideration. In particular when it comes to performing tasks that heavily rely on phase information, such as acoustic beamforming, complex-valued algorithms are omnipresent. In this contribution we recapitulate backpropagation in CVNNs, develop complex-valued network elements, such as the split-rectified non-linearity, and compare real- and complex-valued networks on a beamforming task. We find that CVNNs hardly provide a performance gain and conclude that the effort of developing the complex-valued counterparts of the building blocks of modern deep or recurrent neural networks can hardly be justified.","lang":"eng"}],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/interspeech_2016_drude_slides.pdf","relation":"supplementary_material","description":"Poster"}]},"publication":"INTERSPEECH 2016, San Francisco, USA","citation":{"chicago":"Drude, Lukas, Bhiksha Raj, and Reinhold Haeb-Umbach. “On the Appropriateness of Complex-Valued Neural Networks for Speech Enhancement.” In <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016.","short":"L. Drude, B. Raj, R. Haeb-Umbach, in: INTERSPEECH 2016, San Francisco, USA, 2016.","apa":"Drude, L., Raj, B., &#38; Haeb-Umbach, R. (2016). On the appropriateness of complex-valued neural networks for speech enhancement. In <i>INTERSPEECH 2016, San Francisco, USA</i>.","ieee":"L. Drude, B. Raj, and R. Haeb-Umbach, “On the appropriateness of complex-valued neural networks for speech enhancement,” in <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016.","ama":"Drude L, Raj B, Haeb-Umbach R. On the appropriateness of complex-valued neural networks for speech enhancement. In: <i>INTERSPEECH 2016, San Francisco, USA</i>. ; 2016.","bibtex":"@inproceedings{Drude_Raj_Haeb-Umbach_2016, title={On the appropriateness of complex-valued neural networks for speech enhancement}, booktitle={INTERSPEECH 2016, San Francisco, USA}, author={Drude, Lukas and Raj, Bhiksha and Haeb-Umbach, Reinhold}, year={2016} }","mla":"Drude, Lukas, et al. “On the Appropriateness of Complex-Valued Neural Networks for Speech Enhancement.” <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016."}},{"_id":"11771","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2016/itgspeech2016_08_Glarner.pdf"}],"user_id":"44006","author":[{"first_name":"Thomas","last_name":"Glarner","full_name":"Glarner, Thomas","id":"14169"},{"first_name":"Mohammad","last_name":"Mahdi Momenzadeh","full_name":"Mahdi Momenzadeh, Mohammad"},{"id":"11213","last_name":"Drude","first_name":"Lukas","full_name":"Drude, Lukas"},{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold","id":"242"}],"title":"Factor Graph Decoding for Speech Presence Probability Estimation","year":"2016","status":"public","date_updated":"2022-01-06T06:51:08Z","date_created":"2019-07-12T05:27:56Z","department":[{"_id":"54"}],"oa":"1","type":"conference","citation":{"ieee":"T. Glarner, M. Mahdi Momenzadeh, L. Drude, and R. Haeb-Umbach, “Factor Graph Decoding for Speech Presence Probability Estimation,” in <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","apa":"Glarner, T., Mahdi Momenzadeh, M., Drude, L., &#38; Haeb-Umbach, R. (2016). Factor Graph Decoding for Speech Presence Probability Estimation. In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>.","short":"T. Glarner, M. Mahdi Momenzadeh, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.","chicago":"Glarner, Thomas, Mohammad Mahdi Momenzadeh, Lukas Drude, and Reinhold Haeb-Umbach. “Factor Graph Decoding for Speech Presence Probability Estimation.” In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","mla":"Glarner, Thomas, et al. “Factor Graph Decoding for Speech Presence Probability Estimation.” <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","bibtex":"@inproceedings{Glarner_Mahdi Momenzadeh_Drude_Haeb-Umbach_2016, title={Factor Graph Decoding for Speech Presence Probability Estimation}, booktitle={12. ITG Fachtagung Sprachkommunikation (ITG 2016)}, author={Glarner, Thomas and Mahdi Momenzadeh, Mohammad and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016} }","ama":"Glarner T, Mahdi Momenzadeh M, Drude L, Haeb-Umbach R. Factor Graph Decoding for Speech Presence Probability Estimation. In: <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>. ; 2016."},"publication":"12. ITG Fachtagung Sprachkommunikation (ITG 2016)","related_material":{"link":[{"description":"Slides","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2016/itgspeech2016_08_Glarner_slides.pdf"}]},"abstract":[{"text":"This paper is concerned with speech presence probability estimation employing an explicit model of the temporal and spectral correlations of speech. An undirected graphical model is introduced, based on a Factor Graph formulation. It is shown that this undirected model cures some of the theoretical issues of an earlier directed graphical model. Furthermore, we formulate a message passing inference scheme based on an approximate graph factorization, identify this inference scheme as a particular message passing schedule based on the turbo principle and suggest further alternative schedules. The experiments show an improved performance over speech presence probability estimation based on an IID assumption, and a slightly better performance of the turbo schedule over the alternatives.","lang":"eng"}]},{"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_heymann_paper.pdf","open_access":"1"}],"_id":"11812","language":[{"iso":"eng"}],"user_id":"44006","status":"public","year":"2016","title":"Neural Network Based Spectral Mask Estimation for Acoustic Beamforming","author":[{"full_name":"Heymann, Jahn","last_name":"Heymann","first_name":"Jahn","id":"9168"},{"id":"11213","first_name":"Lukas","last_name":"Drude","full_name":"Drude, Lukas"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_updated":"2022-01-06T06:51:09Z","date_created":"2019-07-12T05:28:44Z","type":"conference","oa":"1","department":[{"_id":"54"}],"publication":"Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)","citation":{"mla":"Heymann, Jahn, et al. “Neural Network Based Spectral Mask Estimation for Acoustic Beamforming.” <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016.","bibtex":"@inproceedings{Heymann_Drude_Haeb-Umbach_2016, title={Neural Network Based Spectral Mask Estimation for Acoustic Beamforming}, booktitle={Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016} }","ama":"Heymann J, Drude L, Haeb-Umbach R. Neural Network Based Spectral Mask Estimation for Acoustic Beamforming. In: <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>. ; 2016.","ieee":"J. Heymann, L. Drude, and R. Haeb-Umbach, “Neural Network Based Spectral Mask Estimation for Acoustic Beamforming,” in <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016.","apa":"Heymann, J., Drude, L., &#38; Haeb-Umbach, R. (2016). Neural Network Based Spectral Mask Estimation for Acoustic Beamforming. In <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>.","short":"J. Heymann, L. Drude, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016.","chicago":"Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Neural Network Based Spectral Mask Estimation for Acoustic Beamforming.” In <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016."},"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_heymann_slides.pdf","relation":"supplementary_material","description":"Slides"}]}},{"abstract":[{"text":"This contribution investigates Direction of Arrival (DoA) estimation using linearly arranged microphone arrays. We are going to develop a model for the DoA estimation error in a reverberant scenario and show the existence of a bias, that is a consequence of the linear arrangement and limited field of view (FoV) bias: First, the limited FoV leading to a clipping of the measurements, and, second, the angular distribution of the signal energy of the reflections being non-uniform. Since both issues are a consequence of the linear arrangement of the sensors, the bias arises largely independent of the kind of DoA estimator. The experimental evaluation demonstrates the existence of the bias for a selected number of DoA estimation methods and proves that the prediction from the developed theoretical model matches the simulation results.","lang":"eng"}],"related_material":{"link":[{"relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2016/JacobHaeb_ITG2016_poster.pdf","description":"Poster"}]},"publication":"12. ITG Fachtagung Sprachkommunikation (ITG 2016)","citation":{"bibtex":"@inproceedings{Jacob_Haeb-Umbach_2016, title={On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays}, booktitle={12. ITG Fachtagung Sprachkommunikation (ITG 2016)}, author={Jacob, Florian and Haeb-Umbach, Reinhold}, year={2016} }","ama":"Jacob F, Haeb-Umbach R. On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays. In: <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>. ; 2016.","mla":"Jacob, Florian, and Reinhold Haeb-Umbach. “On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays.” <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","short":"F. Jacob, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.","chicago":"Jacob, Florian, and Reinhold Haeb-Umbach. “On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays.” In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","ieee":"F. Jacob and R. Haeb-Umbach, “On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays,” in <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.","apa":"Jacob, F., &#38; Haeb-Umbach, R. (2016). On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays. In <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>."},"type":"conference","oa":"1","department":[{"_id":"54"}],"date_created":"2019-07-12T05:29:03Z","date_updated":"2022-01-06T06:51:10Z","year":"2016","title":"On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays","status":"public","author":[{"first_name":"Florian","last_name":"Jacob","full_name":"Jacob, Florian"},{"id":"242","first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold"}],"user_id":"44006","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/JacobHaeb_ITG2016.pdf","open_access":"1"}],"_id":"11829","language":[{"iso":"eng"}]},{"date_created":"2019-07-12T05:29:09Z","type":"conference","department":[{"_id":"54"}],"oa":"1","publication":"Computer Speech and Language","citation":{"mla":"Heymann, Jahn, et al. “Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition.” <i>Computer Speech and Language</i>, 2016.","ama":"Heymann J, Drude L, Haeb-Umbach R. Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition. In: <i>Computer Speech and Language</i>. ; 2016.","bibtex":"@inproceedings{Heymann_Drude_Haeb-Umbach_2016, title={Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition}, booktitle={Computer Speech and Language}, author={Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016} }","apa":"Heymann, J., Drude, L., &#38; Haeb-Umbach, R. (2016). Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition. In <i>Computer Speech and Language</i>.","ieee":"J. Heymann, L. Drude, and R. Haeb-Umbach, “Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition,” in <i>Computer Speech and Language</i>, 2016.","short":"J. Heymann, L. Drude, R. Haeb-Umbach, in: Computer Speech and Language, 2016.","chicago":"Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition.” In <i>Computer Speech and Language</i>, 2016."},"abstract":[{"lang":"eng","text":"We present a system for the 4th CHiME challenge which significantly increases the performance for all three tracks with respect to the provided baseline system. The front-end uses a bi-directional Long Short-Term Memory (BLSTM)-based neural network to estimate signal statistics. These then steer a Generalized Eigenvalue beamformer. The back-end consists of a 22 layer deep Wide Residual Network and two extra BLSTM layers. Working on a whole utterance instead of frames allows us to refine Batch-Normalization. We also train our own BLSTM-based language model. Adding a discriminative speaker adaptation leads to further gains. The final system achieves a word error rate on the six channel real test data of 3.48%. For the two channel track we achieve 5.96% and for the one channel track 9.34%. This is the best reported performance on the challenge achieved by a single system, i.e., a configuration, which does not combine multiple systems. At the same time, our system is independent of the microphone configuration. We can thus use the same components for all three tracks."}],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/chime4_upbonly_poster.pdf","relation":"supplementary_material","description":"Poster"}]},"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2016/chime4_upbonly_paper.pdf","open_access":"1"}],"_id":"11834","language":[{"iso":"eng"}],"user_id":"44006","status":"public","year":"2016","title":"Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition","author":[{"full_name":"Heymann, Jahn","first_name":"Jahn","last_name":"Heymann","id":"9168"},{"id":"11213","first_name":"Lukas","last_name":"Drude","full_name":"Drude, Lukas"},{"id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"date_updated":"2022-01-06T06:51:11Z"}]
