--- _id: '11965' abstract: - lang: eng text: 'We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood criterion derived from a spatial mixture model of the observations. It is trained from scratch without requiring any parallel data consisting of degraded input and clean training targets. Thus, training can be carried out on real recordings of noisy speech rather than simulated ones. In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely. We demonstrate the effectiveness of our approach by speech recognition experiments on two different datasets: one mainly deteriorated by noise (CHiME 4) and one by reverberation (REVERB). The results show that the performance of the proposed system is on par with a supervised system using oracle target masks for training and with a system trained using a model-based teacher.' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Heymann J, Haeb-Umbach R. Unsupervised training of neural mask-based beamforming. In: INTERSPEECH 2019, Graz, Austria. ; 2019.' apa: Drude, L., Heymann, J., & Haeb-Umbach, R. (2019). Unsupervised training of neural mask-based beamforming. In INTERSPEECH 2019, Graz, Austria. bibtex: '@inproceedings{Drude_Heymann_Haeb-Umbach_2019, title={Unsupervised training of neural mask-based beamforming}, booktitle={INTERSPEECH 2019, Graz, Austria}, author={Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2019} }' chicago: Drude, Lukas, Jahn Heymann, and Reinhold Haeb-Umbach. “Unsupervised Training of Neural Mask-Based Beamforming.” In INTERSPEECH 2019, Graz, Austria, 2019. ieee: L. Drude, J. Heymann, and R. Haeb-Umbach, “Unsupervised training of neural mask-based beamforming,” in INTERSPEECH 2019, Graz, Austria, 2019. mla: Drude, Lukas, et al. “Unsupervised Training of Neural Mask-Based Beamforming.” INTERSPEECH 2019, Graz, Austria, 2019. short: 'L. Drude, J. Heymann, R. Haeb-Umbach, in: INTERSPEECH 2019, Graz, Austria, 2019.' date_created: 2019-07-18T09:11:39Z date_updated: 2022-01-06T06:51:14Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-13T06:36:44Z date_updated: 2019-08-13T06:41:35Z file_id: '12914' file_name: INTERSPEECH_2019_Drude_Paper.pdf file_size: 223413 relation: main_file file_date_updated: 2019-08-13T06:41:35Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/publicdomain/zero/1.0/ oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: INTERSPEECH 2019, Graz, Austria status: public title: Unsupervised training of neural mask-based beamforming type: conference user_id: '59789' year: '2019' ... --- _id: '12874' abstract: - lang: eng text: We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26% over the unsupervised teacher. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Daniel full_name: Hasenklever, Daniel last_name: Hasenklever - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Hasenklever D, Haeb-Umbach R. Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation. In: ICASSP 2019, Brighton, UK. ; 2019.' apa: Drude, L., Hasenklever, D., & Haeb-Umbach, R. (2019). Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation. In ICASSP 2019, Brighton, UK. bibtex: '@inproceedings{Drude_Hasenklever_Haeb-Umbach_2019, title={Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation}, booktitle={ICASSP 2019, Brighton, UK}, author={Drude, Lukas and Hasenklever, Daniel and Haeb-Umbach, Reinhold}, year={2019} }' chicago: Drude, Lukas, Daniel Hasenklever, and Reinhold Haeb-Umbach. “Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation.” In ICASSP 2019, Brighton, UK, 2019. ieee: L. Drude, D. Hasenklever, and R. Haeb-Umbach, “Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation,” in ICASSP 2019, Brighton, UK, 2019. mla: Drude, Lukas, et al. “Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation.” ICASSP 2019, Brighton, UK, 2019. short: 'L. Drude, D. Hasenklever, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK, 2019.' date_created: 2019-07-23T07:37:54Z date_updated: 2022-01-06T06:51:21Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-14T07:19:13Z date_updated: 2019-08-14T07:19:13Z file_id: '12925' file_name: ICASSP_2019_Drude_Paper.pdf file_size: 368225 relation: main_file file_date_updated: 2019-08-14T07:19:13Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: ICASSP 2019, Brighton, UK status: public title: Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation type: conference user_id: '59789' year: '2019' ... --- _id: '12875' abstract: - lang: eng text: Signal dereverberation using the Weighted Prediction Error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. First proposed as an iterative algorithm, follow-up works have reformulated it as a recursive least squares algorithm and therefore enabled its use in online applications. For this algorithm, the estimation of the power spectral density (PSD) of the anechoic signal plays an important role and strongly influences its performance. Recently, we showed that using a neural network PSD estimator leads to improved performance for online automatic speech recognition. This, however, comes at a price. To train the network, we require parallel data, i.e., utterances simultaneously available in clean and reverberated form. Here we propose to overcome this limitation by training the network jointly with the acoustic model of the speech recognizer. To be specific, the gradients computed from the cross-entropy loss between the target senone sequence and the acoustic model network output is backpropagated through the complex-valued dereverberation filter estimation to the neural network for PSD estimation. Evaluation on two databases demonstrates improved performance for on-line processing scenarios while imposing fewer requirements on the available training data and thus widening the range of applications. author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Keisuke full_name: Kinoshita, Keisuke last_name: Kinoshita - first_name: Tomohiro full_name: Nakatani, Tomohiro last_name: Nakatani citation: ama: 'Heymann J, Drude L, Haeb-Umbach R, Kinoshita K, Nakatani T. Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR. In: ICASSP 2019, Brighton, UK. ; 2019.' apa: Heymann, J., Drude, L., Haeb-Umbach, R., Kinoshita, K., & Nakatani, T. (2019). Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR. In ICASSP 2019, Brighton, UK. bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_Kinoshita_Nakatani_2019, title={Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR}, booktitle={ICASSP 2019, Brighton, UK}, author={Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani, Tomohiro}, year={2019} }' chicago: Heymann, Jahn, Lukas Drude, Reinhold Haeb-Umbach, Keisuke Kinoshita, and Tomohiro Nakatani. “Joint Optimization of Neural Network-Based WPE Dereverberation and Acoustic Model for Robust Online ASR.” In ICASSP 2019, Brighton, UK, 2019. ieee: J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR,” in ICASSP 2019, Brighton, UK, 2019. mla: Heymann, Jahn, et al. “Joint Optimization of Neural Network-Based WPE Dereverberation and Acoustic Model for Robust Online ASR.” ICASSP 2019, Brighton, UK, 2019. short: 'J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: ICASSP 2019, Brighton, UK, 2019.' date_created: 2019-07-23T07:42:26Z date_updated: 2022-01-06T06:51:22Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-12-17T07:28:06Z date_updated: 2019-12-17T07:28:06Z file_id: '15334' file_name: ICASSP_2019_Heymann_Paper.pdf file_size: 199109 relation: main_file file_date_updated: 2019-12-17T07:28:06Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: ICASSP 2019, Brighton, UK status: public title: Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR type: conference user_id: '59789' year: '2019' ... --- _id: '12876' abstract: - lang: eng text: In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available. Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds. The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend. author: - first_name: Gerhard full_name: Kurz, Gerhard last_name: Kurz - first_name: Igor full_name: Gilitschenski, Igor last_name: Gilitschenski - first_name: Florian full_name: Pfaff, Florian last_name: Pfaff - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Uwe D. full_name: Hanebeck, Uwe D. last_name: Hanebeck - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Roland Y. full_name: Siegwart, Roland Y. last_name: Siegwart citation: ama: 'Kurz G, Gilitschenski I, Pfaff F, et al. Directional Statistics and Filtering Using libDirectional. In: Journal of Statistical Software 89(4). ; 2019.' apa: Kurz, G., Gilitschenski, I., Pfaff, F., Drude, L., Hanebeck, U. D., Haeb-Umbach, R., & Siegwart, R. Y. (2019). Directional Statistics and Filtering Using libDirectional. In Journal of Statistical Software 89(4). bibtex: '@inproceedings{Kurz_Gilitschenski_Pfaff_Drude_Hanebeck_Haeb-Umbach_Siegwart_2019, title={Directional Statistics and Filtering Using libDirectional}, booktitle={Journal of Statistical Software 89(4)}, author={Kurz, Gerhard and Gilitschenski, Igor and Pfaff, Florian and Drude, Lukas and Hanebeck, Uwe D. and Haeb-Umbach, Reinhold and Siegwart, Roland Y.}, year={2019} }' chicago: Kurz, Gerhard, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck, Reinhold Haeb-Umbach, and Roland Y. Siegwart. “Directional Statistics and Filtering Using LibDirectional.” In Journal of Statistical Software 89(4), 2019. ieee: G. Kurz et al., “Directional Statistics and Filtering Using libDirectional,” in Journal of Statistical Software 89(4), 2019. mla: Kurz, Gerhard, et al. “Directional Statistics and Filtering Using LibDirectional.” Journal of Statistical Software 89(4), 2019. short: 'G. Kurz, I. Gilitschenski, F. Pfaff, L. Drude, U.D. Hanebeck, R. Haeb-Umbach, R.Y. Siegwart, in: Journal of Statistical Software 89(4), 2019.' date_created: 2019-07-23T07:44:59Z date_updated: 2022-01-06T06:51:22Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-14T07:16:05Z date_updated: 2019-08-14T07:16:05Z file_id: '12923' file_name: JournalofStatisticalSoftware_2019_Drude_Paper.pdf file_size: 1522964 relation: main_file file_date_updated: 2019-08-14T07:16:05Z has_accepted_license: '1' language: - iso: eng oa: '1' publication: Journal of Statistical Software 89(4) status: public title: Directional Statistics and Filtering Using libDirectional type: conference user_id: '59789' year: '2019' ... --- _id: '12890' abstract: - lang: eng text: 'We formulate a generic framework for blind source separation (BSS), which allows integrating data-driven spectro-temporal methods, such as deep clustering and deep attractor networks, with physically motivated probabilistic spatial methods, such as complex angular central Gaussian mixture models. The integrated model exploits the complementary strengths of the two approaches to BSS: the strong modeling power of neural networks, which, however, is based on supervised learning, and the ease of unsupervised learning of the spatial mixture models whose few parameters can be estimated on as little as a single segment of a real mixture of speech. Experiments are carried out on both artificially mixed speech and true recordings of speech mixtures. The experiments verify that the integrated models consistently outperform the individual components. We further extend the models to cope with noisy, reverberant speech and introduce a cross-domain teacher–student training where the mixture model serves as the teacher to provide training targets for the student neural network.' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: Drude L, Haeb-Umbach R. Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation. IEEE Journal of Selected Topics in Signal Processing. 2019. doi:10.1109/JSTSP.2019.2912565 apa: Drude, L., & Haeb-Umbach, R. (2019). Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation. IEEE Journal of Selected Topics in Signal Processing. https://doi.org/10.1109/JSTSP.2019.2912565 bibtex: '@article{Drude_Haeb-Umbach_2019, title={Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation}, DOI={10.1109/JSTSP.2019.2912565}, journal={IEEE Journal of Selected Topics in Signal Processing}, author={Drude, Lukas and Haeb-Umbach, Reinhold}, year={2019} }' chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE Journal of Selected Topics in Signal Processing, 2019. https://doi.org/10.1109/JSTSP.2019.2912565. ieee: L. Drude and R. Haeb-Umbach, “Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation,” IEEE Journal of Selected Topics in Signal Processing, 2019. mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE Journal of Selected Topics in Signal Processing, 2019, doi:10.1109/JSTSP.2019.2912565. short: L. Drude, R. Haeb-Umbach, IEEE Journal of Selected Topics in Signal Processing (2019). date_created: 2019-07-26T08:38:46Z date_updated: 2022-01-06T06:51:23Z ddc: - '050' department: - _id: '54' doi: 10.1109/JSTSP.2019.2912565 file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-07T07:12:21Z date_updated: 2019-08-14T07:11:22Z file_id: '12903' file_name: IEEE Jounal_2019_Drude_Paper.pdf file_size: 967424 relation: main_file file_date_updated: 2019-08-14T07:11:22Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: IEEE Journal of Selected Topics in Signal Processing publication_identifier: eissn: - 1941-0484 status: public title: Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation type: journal_article user_id: '11213' year: '2019' ... --- _id: '15796' abstract: - lang: eng text: In this paper we consider human daily activity recognition using an acoustic sensor network (ASN) which consists of nodes distributed in a home environment. Assuming that the ASN is permanently recording, the vast majority of recordings is silence. Therefore, we propose to employ a computationally efficient two-stage sound recognition system, consisting of an initial sound activity detection (SAD) and a subsequent sound event classification (SEC), which is only activated once sound activity has been detected. We show how a low-latency activity detector with high temporal resolution can be trained from weak labels with low temporal resolution. We further demonstrate the advantage of using spatial features for the subsequent event classification task. author: - first_name: Janek full_name: Ebbers, Janek id: '34851' last_name: Ebbers - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Andreas full_name: Brendel, Andreas last_name: Brendel - first_name: Walter full_name: Kellermann, Walter last_name: Kellermann citation: ama: 'Ebbers J, Drude L, Haeb-Umbach R, Brendel A, Kellermann W. Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks. In: CAMSAP 2019, Guadeloupe, West Indies. ; 2019.' apa: Ebbers, J., Drude, L., Haeb-Umbach, R., Brendel, A., & Kellermann, W. (2019). Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks. CAMSAP 2019, Guadeloupe, West Indies. bibtex: '@inproceedings{Ebbers_Drude_Haeb-Umbach_Brendel_Kellermann_2019, title={Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks}, booktitle={CAMSAP 2019, Guadeloupe, West Indies}, author={Ebbers, Janek and Drude, Lukas and Haeb-Umbach, Reinhold and Brendel, Andreas and Kellermann, Walter}, year={2019} }' chicago: Ebbers, Janek, Lukas Drude, Reinhold Haeb-Umbach, Andreas Brendel, and Walter Kellermann. “Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks.” In CAMSAP 2019, Guadeloupe, West Indies, 2019. ieee: J. Ebbers, L. Drude, R. Haeb-Umbach, A. Brendel, and W. Kellermann, “Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks,” 2019. mla: Ebbers, Janek, et al. “Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks.” CAMSAP 2019, Guadeloupe, West Indies, 2019. short: 'J. Ebbers, L. Drude, R. Haeb-Umbach, A. Brendel, W. Kellermann, in: CAMSAP 2019, Guadeloupe, West Indies, 2019.' date_created: 2020-02-05T10:20:17Z date_updated: 2023-11-22T08:29:58Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2020-02-05T10:21:39Z date_updated: 2020-02-05T10:21:39Z file_id: '15797' file_name: CAMSAP_2019_WS_Ebbers_Paper.pdf file_size: 311887 relation: main_file file_date_updated: 2020-02-05T10:21:39Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: CAMSAP 2019, Guadeloupe, West Indies quality_controlled: '1' status: public title: Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks type: conference user_id: '34851' year: '2019' ... --- _id: '11835' abstract: - lang: eng text: Signal dereverberation using the weighted prediction error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. But in its original formulation, WPE requires multiple iterations over a sufficiently long utterance, rendering it unsuitable for online low-latency applications. Recently, two methods have been proposed to overcome this limitation. One utilizes a neural network to estimate the power spectral density (PSD) of the target signal and works in a block-online fashion. The other method relies on a rather simple PSD estimation which smoothes the observed PSD and utilizes a recursive formulation which enables it to work on a frame-by-frame basis. In this paper, we integrate a deep neural network (DNN) based estimator into the recursive frame-online formulation. We evaluate the performance of the recursive system with different PSD estimators in comparison to the block-online and offline variant on two distinct corpora. The REVERB challenge data, where the signal is mainly deteriorated by reverberation, and a database which combines WSJ and VoiceHome to also consider (directed) noise sources. The results show that although smoothing works surprisingly well, the more sophisticated DNN based estimator shows promising improvements and shortens the performance gap between online and offline processing. author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Keisuke full_name: Kinoshita, Keisuke last_name: Kinoshita - first_name: Tomohiro full_name: Nakatani, Tomohiro last_name: Nakatani citation: ama: 'Heymann J, Drude L, Haeb-Umbach R, Kinoshita K, Nakatani T. Frame-Online DNN-WPE Dereverberation. In: IWAENC 2018, Tokio, Japan. ; 2018.' apa: Heymann, J., Drude, L., Haeb-Umbach, R., Kinoshita, K., & Nakatani, T. (2018). Frame-Online DNN-WPE Dereverberation. In IWAENC 2018, Tokio, Japan. bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_Kinoshita_Nakatani_2018, title={Frame-Online DNN-WPE Dereverberation}, booktitle={IWAENC 2018, Tokio, Japan}, author={Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani, Tomohiro}, year={2018} }' chicago: Heymann, Jahn, Lukas Drude, Reinhold Haeb-Umbach, Keisuke Kinoshita, and Tomohiro Nakatani. “Frame-Online DNN-WPE Dereverberation.” In IWAENC 2018, Tokio, Japan, 2018. ieee: J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Frame-Online DNN-WPE Dereverberation,” in IWAENC 2018, Tokio, Japan, 2018. mla: Heymann, Jahn, et al. “Frame-Online DNN-WPE Dereverberation.” IWAENC 2018, Tokio, Japan, 2018. short: 'J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: IWAENC 2018, Tokio, Japan, 2018.' date_created: 2019-07-12T05:29:10Z date_updated: 2022-01-06T06:51:11Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2018/IWAENC_2018_Heymann_Paper.pdf oa: '1' publication: IWAENC 2018, Tokio, Japan related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2018/IWAENC_2018_Heymann_Poster.pdf status: public title: Frame-Online DNN-WPE Dereverberation type: conference user_id: '44006' year: '2018' ... --- _id: '11872' abstract: - lang: eng text: 'The weighted prediction error (WPE) algorithm has proven to be a very successful dereverberation method for the REVERB challenge. Likewise, neural network based mask estimation for beamforming demonstrated very good noise suppression in the CHiME 3 and CHiME 4 challenges. Recently, it has been shown that this estimator can also be trained to perform dereverberation and denoising jointly. However, up to now a comparison of a neural beamformer and WPE is still missing, so is an investigation into a combination of the two. Therefore, we here provide an extensive evaluation of both and consequently propose variants to integrate deep neural network based beamforming with WPE. For these integrated variants we identify a consistent word error rate (WER) reduction on two distinct databases. In particular, our study shows that deep learning based beamforming benefits from a model-based dereverberation technique (i.e. WPE) and vice versa. Our key findings are: (a) Neural beamforming yields the lower WERs in comparison to WPE the more channels and noise are present. (b) Integration of WPE and a neural beamformer consistently outperforms all stand-alone systems.' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Keisuke full_name: Kinoshita, Keisuke last_name: Kinoshita - first_name: Marc full_name: Delcroix, Marc last_name: Delcroix - first_name: Tomohiro full_name: Nakatani, Tomohiro last_name: Nakatani - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Boeddeker C, Heymann J, et al. Integration neural network based beamforming and weighted prediction error dereverberation. In: INTERSPEECH 2018, Hyderabad, India. ; 2018.' apa: Drude, L., Boeddeker, C., Heymann, J., Kinoshita, K., Delcroix, M., Nakatani, T., & Haeb-Umbach, R. (2018). Integration neural network based beamforming and weighted prediction error dereverberation. In INTERSPEECH 2018, Hyderabad, India. bibtex: '@inproceedings{Drude_Boeddeker_Heymann_Kinoshita_Delcroix_Nakatani_Haeb-Umbach_2018, title={Integration neural network based beamforming and weighted prediction error dereverberation}, booktitle={INTERSPEECH 2018, Hyderabad, India}, author={Drude, Lukas and Boeddeker, Christoph and Heymann, Jahn and Kinoshita, Keisuke and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}, year={2018} }' chicago: Drude, Lukas, Christoph Boeddeker, Jahn Heymann, Keisuke Kinoshita, Marc Delcroix, Tomohiro Nakatani, and Reinhold Haeb-Umbach. “Integration Neural Network Based Beamforming and Weighted Prediction Error Dereverberation.” In INTERSPEECH 2018, Hyderabad, India, 2018. ieee: L. Drude et al., “Integration neural network based beamforming and weighted prediction error dereverberation,” in INTERSPEECH 2018, Hyderabad, India, 2018. mla: Drude, Lukas, et al. “Integration Neural Network Based Beamforming and Weighted Prediction Error Dereverberation.” INTERSPEECH 2018, Hyderabad, India, 2018. short: 'L. Drude, C. Boeddeker, J. Heymann, K. Kinoshita, M. Delcroix, T. Nakatani, R. Haeb-Umbach, in: INTERSPEECH 2018, Hyderabad, India, 2018.' date_created: 2019-07-12T05:29:53Z date_updated: 2022-01-06T06:51:11Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Drude_Paper.pdf oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: INTERSPEECH 2018, Hyderabad, India related_material: link: - description: Slides relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Drude_Slides.pdf status: public title: Integration neural network based beamforming and weighted prediction error dereverberation type: conference user_id: '40767' year: '2018' ... --- _id: '11873' abstract: - lang: eng text: NARA-WPE is a Python software package providing implementations of the weighted prediction error (WPE) dereverberation algorithm. WPE has been shown to be a highly effective tool for speech dereverberation, thus improving the perceptual quality of the signal and improving the recognition performance of downstream automatic speech recognition (ASR). It is suitable both for single-channel and multi-channel applications. The package consist of (1) a Numpy implementation which can easily be integrated into a custom Python toolchain, and (2) a TensorFlow implementation which allows integration into larger computational graphs and enables backpropagation through WPE to train more advanced front-ends. This package comprises of an iterative offline (batch) version, a block-online version, and a frame-online version which can be used in moderately low latency applications, e.g. digital speech assistants. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Heymann J, Boeddeker C, Haeb-Umbach R. NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing. In: ITG 2018, Oldenburg, Germany. ; 2018.' apa: 'Drude, L., Heymann, J., Boeddeker, C., & Haeb-Umbach, R. (2018). NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing. In ITG 2018, Oldenburg, Germany.' bibtex: '@inproceedings{Drude_Heymann_Boeddeker_Haeb-Umbach_2018, title={NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing}, booktitle={ITG 2018, Oldenburg, Germany}, author={Drude, Lukas and Heymann, Jahn and Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2018} }' chicago: 'Drude, Lukas, Jahn Heymann, Christoph Boeddeker, and Reinhold Haeb-Umbach. “NARA-WPE: A Python Package for Weighted Prediction Error Dereverberation in Numpy and Tensorflow for Online and Offline Processing.” In ITG 2018, Oldenburg, Germany, 2018.' ieee: 'L. Drude, J. Heymann, C. Boeddeker, and R. Haeb-Umbach, “NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing,” in ITG 2018, Oldenburg, Germany, 2018.' mla: 'Drude, Lukas, et al. “NARA-WPE: A Python Package for Weighted Prediction Error Dereverberation in Numpy and Tensorflow for Online and Offline Processing.” ITG 2018, Oldenburg, Germany, 2018.' short: 'L. Drude, J. Heymann, C. Boeddeker, R. Haeb-Umbach, in: ITG 2018, Oldenburg, Germany, 2018.' date_created: 2019-07-12T05:29:54Z date_updated: 2022-01-06T06:51:11Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Drude_Paper.pdf oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: ITG 2018, Oldenburg, Germany related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Drude_Poster.pdf status: public title: 'NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing' type: conference user_id: '40767' year: '2018' ... --- _id: '12898' abstract: - lang: eng text: Deep clustering (DC) and deep attractor networks (DANs) are a data-driven way to monaural blind source separation. Both approaches provide astonishing single channel performance but have not yet been generalized to block-online processing. When separating speech in a continuous stream with a block-online algorithm, it needs to be determined in each block which of the output streams belongs to whom. In this contribution we solve this block permutation problem by introducing an additional speaker identification embedding to the DAN model structure. We motivate this model decision by analyzing the embedding topology of DC and DANs and show, that DC and DANs themselves are not sufficient for speaker identification. This model structure (a) improves the signal to distortion ratio (SDR) over a DAN baseline and (b) provides up to 61% and up to 34% relative reduction in permutation error rate and re-identification error rate compared to an i-vector baseline, respectively. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Thilo full_name: von Neumann, Thilo last_name: von Neumann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, von Neumann T, Haeb-Umbach R. Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation. In: ICASSP 2018, Calgary, Canada. ; 2018.' apa: Drude, L., von Neumann, T., & Haeb-Umbach, R. (2018). Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation. In ICASSP 2018, Calgary, Canada. bibtex: '@inproceedings{Drude_von Neumann_Haeb-Umbach_2018, title={Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation}, booktitle={ICASSP 2018, Calgary, Canada}, author={Drude, Lukas and von Neumann, Thilo and Haeb-Umbach, Reinhold}, year={2018} }' chicago: Drude, Lukas, Thilo von Neumann, and Reinhold Haeb-Umbach. “Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation.” In ICASSP 2018, Calgary, Canada, 2018. ieee: L. Drude, T. von Neumann, and R. Haeb-Umbach, “Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation,” in ICASSP 2018, Calgary, Canada, 2018. mla: Drude, Lukas, et al. “Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation.” ICASSP 2018, Calgary, Canada, 2018. short: 'L. Drude, T. von Neumann, R. Haeb-Umbach, in: ICASSP 2018, Calgary, Canada, 2018.' date_created: 2019-07-30T14:22:53Z date_updated: 2022-01-06T06:51:24Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude2_Paper.pdf oa: '1' publication: ICASSP 2018, Calgary, Canada related_material: link: - description: Slides relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude2_Slides.pdf status: public title: Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation type: conference user_id: '44006' year: '2018' ... --- _id: '12900' abstract: - lang: eng text: 'Deep attractor networks (DANs) are a recently introduced method to blindly separate sources from spectral features of a monaural recording using bidirectional long short-term memory networks (BLSTMs). Due to the nature of BLSTMs, this is inherently not online-ready and resorting to operating on blocks yields a block permutation problem in that the index of each speaker may change between blocks. We here propose the joint modeling of spatial and spectral features to solve the block permutation problem and generalize DANs to multi-channel meeting recordings: The DAN acts as a spectral feature extractor for a subsequent model-based clustering approach. We first analyze different joint models in batch-processing scenarios and finally propose a block-online blind source separation algorithm. The efficacy of the proposed models is demonstrated on reverberant mixtures corrupted by real recordings of multi-channel background noise. We demonstrate that both the proposed batch-processing and the proposed block-online system outperform (a) a spatial-only model with a state-of-the-art frequency permutation solver and (b) a spectral-only model with an oracle block permutation solver in terms of signal to distortion ratio (SDR) gains.' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: ' Takuya ' full_name: 'Higuchi,, Takuya ' last_name: Higuchi, - first_name: 'Keisuke ' full_name: 'Kinoshita, Keisuke ' last_name: Kinoshita - first_name: 'Tomohiro ' full_name: 'Nakatani, Tomohiro ' last_name: Nakatani - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Higuchi, Takuya , Kinoshita K, Nakatani T, Haeb-Umbach R. Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation. In: ICASSP 2018, Calgary, Canada. ; 2018.' apa: Drude, L., Higuchi, Takuya , Kinoshita, K., Nakatani, T., & Haeb-Umbach, R. (2018). Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation. In ICASSP 2018, Calgary, Canada. bibtex: '@inproceedings{Drude_Higuchi,_Kinoshita_Nakatani_Haeb-Umbach_2018, title={Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation}, booktitle={ICASSP 2018, Calgary, Canada}, author={Drude, Lukas and Higuchi, Takuya and Kinoshita, Keisuke and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}, year={2018} }' chicago: Drude, Lukas, Takuya Higuchi, Keisuke Kinoshita, Tomohiro Nakatani, and Reinhold Haeb-Umbach. “Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation.” In ICASSP 2018, Calgary, Canada, 2018. ieee: L. Drude, Takuya Higuchi, K. Kinoshita, T. Nakatani, and R. Haeb-Umbach, “Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation,” in ICASSP 2018, Calgary, Canada, 2018. mla: Drude, Lukas, et al. “Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation.” ICASSP 2018, Calgary, Canada, 2018. short: 'L. Drude, Takuya Higuchi, K. Kinoshita, T. Nakatani, R. Haeb-Umbach, in: ICASSP 2018, Calgary, Canada, 2018.' date_created: 2019-07-30T14:42:15Z date_updated: 2022-01-06T06:51:24Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude_Paper.pdf oa: '1' publication: ICASSP 2018, Calgary, Canada related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude_Poster.pdf status: public title: Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation type: conference user_id: '44006' year: '2018' ... --- _id: '12899' abstract: - lang: eng text: This contribution presents a speech enhancement system for the CHiME-5 Dinner Party Scenario. The front-end employs multi-channel linear time-variant filtering and achieves its gains without the use of a neural network. We present an adaptation of blind source separation techniques to the CHiME-5 database which we call Guided Source Separation (GSS). Using the baseline acoustic and language model, the combination of Weighted Prediction Error based dereverberation, guided source separation, and beamforming reduces the WER by 10:54% (relative) for the single array track and by 21:12% (relative) on the multiple array track. author: - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Jens full_name: Heitkaemper, Jens id: '27643' last_name: Heitkaemper - first_name: Joerg full_name: Schmalenstroeer, Joerg id: '460' last_name: Schmalenstroeer - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Jahn full_name: Heymann, Jahn last_name: Heymann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Boeddeker C, Heitkaemper J, Schmalenstroeer J, Drude L, Heymann J, Haeb-Umbach R. Front-End Processing for the CHiME-5 Dinner Party Scenario. In: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India. ; 2018.' apa: Boeddeker, C., Heitkaemper, J., Schmalenstroeer, J., Drude, L., Heymann, J., & Haeb-Umbach, R. (2018). Front-End Processing for the CHiME-5 Dinner Party Scenario. Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India. bibtex: '@inproceedings{Boeddeker_Heitkaemper_Schmalenstroeer_Drude_Heymann_Haeb-Umbach_2018, title={Front-End Processing for the CHiME-5 Dinner Party Scenario}, booktitle={Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India}, author={Boeddeker, Christoph and Heitkaemper, Jens and Schmalenstroeer, Joerg and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2018} }' chicago: Boeddeker, Christoph, Jens Heitkaemper, Joerg Schmalenstroeer, Lukas Drude, Jahn Heymann, and Reinhold Haeb-Umbach. “Front-End Processing for the CHiME-5 Dinner Party Scenario.” In Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India, 2018. ieee: C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann, and R. Haeb-Umbach, “Front-End Processing for the CHiME-5 Dinner Party Scenario,” 2018. mla: Boeddeker, Christoph, et al. “Front-End Processing for the CHiME-5 Dinner Party Scenario.” Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India, 2018. short: 'C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann, R. Haeb-Umbach, in: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India, 2018.' date_created: 2019-07-30T14:35:15Z date_updated: 2023-10-26T08:14:15Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Paper.pdf oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India quality_controlled: '1' related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Poster.pdf status: public title: Front-End Processing for the CHiME-5 Dinner Party Scenario type: conference user_id: '460' year: '2018' ... --- _id: '11876' abstract: - lang: eng text: This paper describes the systems for the single-array track and the multiple-array track of the 5th CHiME Challenge. The final system is a combination of multiple systems, using Confusion Network Combination (CNC). The different systems presented here are utilizing different front-ends and training sets for a Bidirectional Long Short-Term Memory (BLSTM) Acoustic Model (AM). The front-end was replaced by enhancements provided by Paderborn University [1]. The back-end has been implemented using RASR [2] and RETURNN [3]. Additionally, a system combination including the hypothesis word graphs from the system of the submission [1] has been performed, which results in the final best system. author: - first_name: Markus full_name: Kitza, Markus last_name: Kitza - first_name: Wilfried full_name: Michel, Wilfried last_name: Michel - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Jens full_name: Heitkaemper, Jens id: '27643' last_name: Heitkaemper - first_name: Tobias full_name: Menne, Tobias last_name: Menne - first_name: Ralf full_name: Schlüter, Ralf last_name: Schlüter - first_name: Hermann full_name: Ney, Hermann last_name: Ney - first_name: Joerg full_name: Schmalenstroeer, Joerg id: '460' last_name: Schmalenstroeer - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Kitza M, Michel W, Boeddeker C, et al. The RWTH/UPB System Combination for the CHiME 2018 Workshop. In: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India. ; 2018.' apa: Kitza, M., Michel, W., Boeddeker, C., Heitkaemper, J., Menne, T., Schlüter, R., Ney, H., Schmalenstroeer, J., Drude, L., Heymann, J., & Haeb-Umbach, R. (2018). The RWTH/UPB System Combination for the CHiME 2018 Workshop. Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India. bibtex: '@inproceedings{Kitza_Michel_Boeddeker_Heitkaemper_Menne_Schlüter_Ney_Schmalenstroeer_Drude_Heymann_et al._2018, title={The RWTH/UPB System Combination for the CHiME 2018 Workshop}, booktitle={Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India}, author={Kitza, Markus and Michel, Wilfried and Boeddeker, Christoph and Heitkaemper, Jens and Menne, Tobias and Schlüter, Ralf and Ney, Hermann and Schmalenstroeer, Joerg and Drude, Lukas and Heymann, Jahn and et al.}, year={2018} }' chicago: Kitza, Markus, Wilfried Michel, Christoph Boeddeker, Jens Heitkaemper, Tobias Menne, Ralf Schlüter, Hermann Ney, et al. “The RWTH/UPB System Combination for the CHiME 2018 Workshop.” In Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India, 2018. ieee: M. Kitza et al., “The RWTH/UPB System Combination for the CHiME 2018 Workshop,” 2018. mla: Kitza, Markus, et al. “The RWTH/UPB System Combination for the CHiME 2018 Workshop.” Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India, 2018. short: 'M. Kitza, W. Michel, C. Boeddeker, J. Heitkaemper, T. Menne, R. Schlüter, H. Ney, J. Schmalenstroeer, L. Drude, J. Heymann, R. Haeb-Umbach, in: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India, 2018.' date_created: 2019-07-12T05:29:58Z date_updated: 2023-10-26T08:12:14Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_RWTH_Paper.pdf oa: '1' publication: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India quality_controlled: '1' status: public title: The RWTH/UPB System Combination for the CHiME 2018 Workshop type: conference user_id: '460' year: '2018' ... --- _id: '11735' abstract: - lang: eng text: This report describes the computation of gradients by algorithmic differentiation for statistically optimum beamforming operations. Especially the derivation of complex-valued functions is a key component of this approach. Therefore the real-valued algorithmic differentiation is extended via the complex-valued chain rule. In addition to the basic mathematic operations the derivative of the eigenvalue problem with complex-valued eigenvectors is one of the key results of this report. The potential of this approach is shown with experimental results on the CHiME-3 challenge database. There, the beamforming task is used as a front-end for an ASR system. With the developed derivatives a joint optimization of a speech enhancement and speech recognition system w.r.t. the recognition optimization criterion is possible. author: - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Patrick full_name: Hanebrink, Patrick last_name: Hanebrink - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: Boeddeker C, Hanebrink P, Drude L, Heymann J, Haeb-Umbach R. On the Computation of Complex-Valued Gradients with Application to Statistically Optimum Beamforming.; 2017. apa: Boeddeker, C., Hanebrink, P., Drude, L., Heymann, J., & Haeb-Umbach, R. (2017). On the Computation of Complex-valued Gradients with Application to Statistically Optimum Beamforming. bibtex: '@book{Boeddeker_Hanebrink_Drude_Heymann_Haeb-Umbach_2017, title={On the Computation of Complex-valued Gradients with Application to Statistically Optimum Beamforming}, author={Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2017} }' chicago: Boeddeker, Christoph, Patrick Hanebrink, Lukas Drude, Jahn Heymann, and Reinhold Haeb-Umbach. On the Computation of Complex-Valued Gradients with Application to Statistically Optimum Beamforming, 2017. ieee: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, and R. Haeb-Umbach, On the Computation of Complex-valued Gradients with Application to Statistically Optimum Beamforming. 2017. mla: Boeddeker, Christoph, et al. On the Computation of Complex-Valued Gradients with Application to Statistically Optimum Beamforming. 2017. short: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, R. Haeb-Umbach, On the Computation of Complex-Valued Gradients with Application to Statistically Optimum Beamforming, 2017. date_created: 2019-07-12T05:27:15Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2017/ArXiv_2017_BoeddekerHanebrinkHaeb_Article.pdf oa: '1' status: public title: On the Computation of Complex-valued Gradients with Application to Statistically Optimum Beamforming type: report user_id: '40767' year: '2017' ... --- _id: '11736' abstract: - lang: eng text: In this paper we show how a neural network for spectral mask estimation for an acoustic beamformer can be optimized by algorithmic differentiation. Using the beamformer output SNR as the objective function to maximize, the gradient is propagated through the beamformer all the way to the neural network which provides the clean speech and noise masks from which the beamformer coefficients are estimated by eigenvalue decomposition. A key theoretical result is the derivative of an eigenvalue problem involving complex-valued eigenvectors. Experimental results on the CHiME-3 challenge database demonstrate the effectiveness of the approach. The tools developed in this paper are a key component for an end-to-end optimization of speech enhancement and speech recognition. author: - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Patrick full_name: Hanebrink, Patrick last_name: Hanebrink - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Boeddeker C, Hanebrink P, Drude L, Heymann J, Haeb-Umbach R. Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation. In: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). ; 2017.' apa: Boeddeker, C., Hanebrink, P., Drude, L., Heymann, J., & Haeb-Umbach, R. (2017). Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). bibtex: '@inproceedings{Boeddeker_Hanebrink_Drude_Heymann_Haeb-Umbach_2017, title={Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation}, booktitle={Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2017} }' chicago: Boeddeker, Christoph, Patrick Hanebrink, Lukas Drude, Jahn Heymann, and Reinhold Haeb-Umbach. “Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation.” In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017. ieee: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, and R. Haeb-Umbach, “Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation,” in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017. mla: Boeddeker, Christoph, et al. “Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation.” Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017. short: 'C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.' date_created: 2019-07-12T05:27:16Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2017/icassp_2017_boeddeker_paper.pdf oa: '1' publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) status: public title: Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation type: conference user_id: '44006' year: '2017' ... --- _id: '11754' abstract: - lang: eng text: Recent advances in discriminatively trained mask estimation networks to extract a single source utilizing beamforming techniques demonstrate, that the integration of statistical models and deep neural networks (DNNs) are a promising approach for robust automatic speech recognition (ASR) applications. In this contribution we demonstrate how discriminatively trained embeddings on spectral features can be tightly integrated into statistical model-based source separation to separate and transcribe overlapping speech. Good generalization to unseen spatial configurations is achieved by estimating a statistical model at test time, while still leveraging discriminative training of deep clustering embeddings on a separate training set. We formulate an expectation maximization (EM) algorithm which jointly estimates a model for deep clustering embeddings and complex-valued spatial observations in the short time Fourier transform (STFT) domain at test time. Extensive simulations confirm, that the integrated model outperforms (a) a deep clustering model with a subsequent beamforming step and (b) an EM-based model with a beamforming step alone in terms of signal to distortion ratio (SDR) and perceptually motivated metric (PESQ) gains. ASR results on a reverberated dataset further show, that the aforementioned gains translate to reduced word error rates (WERs) even in reverberant environments. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Haeb-Umbach R. Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings. In: INTERSPEECH 2017, Stockholm, Schweden. ; 2017.' apa: Drude, L., & Haeb-Umbach, R. (2017). Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings. In INTERSPEECH 2017, Stockholm, Schweden. bibtex: '@inproceedings{Drude_Haeb-Umbach_2017, title={Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings}, booktitle={INTERSPEECH 2017, Stockholm, Schweden}, author={Drude, Lukas and Haeb-Umbach, Reinhold}, year={2017} }' chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Tight Integration of Spatial and Spectral Features for BSS with Deep Clustering Embeddings.” In INTERSPEECH 2017, Stockholm, Schweden, 2017. ieee: L. Drude and R. Haeb-Umbach, “Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings,” in INTERSPEECH 2017, Stockholm, Schweden, 2017. mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Tight Integration of Spatial and Spectral Features for BSS with Deep Clustering Embeddings.” INTERSPEECH 2017, Stockholm, Schweden, 2017. short: 'L. Drude, R. Haeb-Umbach, in: INTERSPEECH 2017, Stockholm, Schweden, 2017.' date_created: 2019-07-12T05:27:37Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Drude_paper.pdf oa: '1' publication: INTERSPEECH 2017, Stockholm, Schweden related_material: link: - description: Slides relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Drude_slides.pdf status: public title: Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings type: conference user_id: '44006' year: '2017' ... --- _id: '11809' abstract: - lang: eng text: This paper presents an end-to-end training approach for a beamformer-supported multi-channel ASR system. A neural network which estimates masks for a statistically optimum beamformer is jointly trained with a network for acoustic modeling. To update its parameters, we propagate the gradients from the acoustic model all the way through feature extraction and the complex valued beamforming operation. Besides avoiding a mismatch between the front-end and the back-end, this approach also eliminates the need for stereo data, i.e., the parallel availability of clean and noisy versions of the signals. Instead, it can be trained with real noisy multichannel data only. Also, relying on the signal statistics for beamforming, the approach makes no assumptions on the configuration of the microphone array. We further observe a performance gain through joint training in terms of word error rate in an evaluation of the system on the CHiME 4 dataset. author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Patrick full_name: Hanebrink, Patrick last_name: Hanebrink - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Heymann J, Drude L, Boeddeker C, Hanebrink P, Haeb-Umbach R. BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System. In: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). ; 2017.' apa: 'Heymann, J., Drude, L., Boeddeker, C., Hanebrink, P., & Haeb-Umbach, R. (2017). BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP).' bibtex: '@inproceedings{Heymann_Drude_Boeddeker_Hanebrink_Haeb-Umbach_2017, title={BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System}, booktitle={Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Heymann, Jahn and Drude, Lukas and Boeddeker, Christoph and Hanebrink, Patrick and Haeb-Umbach, Reinhold}, year={2017} }' chicago: 'Heymann, Jahn, Lukas Drude, Christoph Boeddeker, Patrick Hanebrink, and Reinhold Haeb-Umbach. “BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System.” In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.' ieee: 'J. Heymann, L. Drude, C. Boeddeker, P. Hanebrink, and R. Haeb-Umbach, “BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System,” in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.' mla: 'Heymann, Jahn, et al. “BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System.” Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.' short: 'J. Heymann, L. Drude, C. Boeddeker, P. Hanebrink, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.' date_created: 2019-07-12T05:28:40Z date_updated: 2022-01-06T06:51:09Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2017/icassp_2017_heymann_paper.pdf oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2017/icassp_2017_heymann_poster.pdf status: public title: 'BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System' type: conference user_id: '40767' year: '2017' ... --- _id: '11811' abstract: - lang: eng text: 'Acoustic beamforming can greatly improve the performance of Automatic Speech Recognition (ASR) and speech enhancement systems when multiple channels are available. We recently proposed a way to support the model-based Generalized Eigenvalue beamforming operation with a powerful neural network for spectral mask estimation. The enhancement system has a number of desirable properties. In particular, neither assumptions need to be made about the nature of the acoustic transfer function (e.g., being anechonic), nor does the array configuration need to be known. While the system has been originally developed to enhance speech in noisy environments, we show in this article that it is also effective in suppressing reverberation, thus leading to a generic trainable multi-channel speech enhancement system for robust speech processing. To support this claim, we consider two distinct datasets: The CHiME 3 challenge, which features challenging real-world noise distortions, and the Reverb challenge, which focuses on distortions caused by reverberation. We evaluate the system both with respect to a speech enhancement and a recognition task. For the first task we propose a new way to cope with the distortions introduced by the Generalized Eigenvalue beamformer by renormalizing the target energy for each frequency bin, and measure its effectiveness in terms of the PESQ score. For the latter we feed the enhanced signal to a strong DNN back-end and achieve state-of-the-art ASR results on both datasets. We further experiment with different network architectures for spectral mask estimation: One small feed-forward network with only one hidden layer, one Convolutional Neural Network and one bi-directional Long Short-Term Memory network, showing that even a small network is capable of delivering significant performance improvements.' author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: Heymann J, Drude L, Haeb-Umbach R. A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing. Computer Speech and Language. 2017. apa: Heymann, J., Drude, L., & Haeb-Umbach, R. (2017). A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing. Computer Speech and Language. bibtex: '@article{Heymann_Drude_Haeb-Umbach_2017, title={A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing}, journal={Computer Speech and Language}, author={Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2017} }' chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing.” Computer Speech and Language, 2017. ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing,” Computer Speech and Language, 2017. mla: Heymann, Jahn, et al. “A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing.” Computer Speech and Language, 2017. short: J. Heymann, L. Drude, R. Haeb-Umbach, Computer Speech and Language (2017). date_created: 2019-07-12T05:28:43Z date_updated: 2022-01-06T06:51:09Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2017/ComputerSpeechLanguage_2017_heymann_paper.pdf oa: '1' publication: Computer Speech and Language status: public title: A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing type: journal_article user_id: '44006' year: '2017' ... --- _id: '11759' abstract: - lang: eng text: 'Variational Autoencoders (VAEs) have been shown to provide efficient neural-network-based approximate Bayesian inference for observation models for which exact inference is intractable. Its extension, the so-called Structured VAE (SVAE) allows inference in the presence of both discrete and continuous latent variables. Inspired by this extension, we developed a VAE with Hidden Markov Models (HMMs) as latent models. We applied the resulting HMM-VAE to the task of acoustic unit discovery in a zero resource scenario. Starting from an initial model based on variational inference in an HMM with Gaussian Mixture Model (GMM) emission probabilities, the accuracy of the acoustic unit discovery could be significantly improved by the HMM-VAE. In doing so we were able to demonstrate for an unsupervised learning task what is well-known in the supervised learning case: Neural networks provide superior modeling power compared to GMMs.' author: - first_name: Janek full_name: Ebbers, Janek id: '34851' last_name: Ebbers - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Thomas full_name: Glarner, Thomas id: '14169' last_name: Glarner - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Bhiksha full_name: Raj, Bhiksha last_name: Raj citation: ama: 'Ebbers J, Heymann J, Drude L, Glarner T, Haeb-Umbach R, Raj B. Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery. In: INTERSPEECH 2017, Stockholm, Schweden. ; 2017.' apa: Ebbers, J., Heymann, J., Drude, L., Glarner, T., Haeb-Umbach, R., & Raj, B. (2017). Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery. INTERSPEECH 2017, Stockholm, Schweden. bibtex: '@inproceedings{Ebbers_Heymann_Drude_Glarner_Haeb-Umbach_Raj_2017, title={Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery}, booktitle={INTERSPEECH 2017, Stockholm, Schweden}, author={Ebbers, Janek and Heymann, Jahn and Drude, Lukas and Glarner, Thomas and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2017} }' chicago: Ebbers, Janek, Jahn Heymann, Lukas Drude, Thomas Glarner, Reinhold Haeb-Umbach, and Bhiksha Raj. “Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.” In INTERSPEECH 2017, Stockholm, Schweden, 2017. ieee: J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, and B. Raj, “Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2017. mla: Ebbers, Janek, et al. “Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.” INTERSPEECH 2017, Stockholm, Schweden, 2017. short: 'J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, B. Raj, in: INTERSPEECH 2017, Stockholm, Schweden, 2017.' date_created: 2019-07-12T05:27:42Z date_updated: 2023-11-22T08:29:06Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_paper.pdf oa: '1' publication: INTERSPEECH 2017, Stockholm, Schweden quality_controlled: '1' related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_poster.pdf - description: Slides relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_slides.pdf status: public title: Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery type: conference user_id: '34851' year: '2017' ... --- _id: '11895' abstract: - lang: eng text: Multi-channel speech enhancement algorithms rely on a synchronous sampling of the microphone signals. This, however, cannot always be guaranteed, especially if the sensors are distributed in an environment. To avoid performance degradation the sampling rate offset needs to be estimated and compensated for. In this contribution we extend the recently proposed coherence drift based method in two important directions. First, the increasing phase shift in the short-time Fourier transform domain is estimated from the coherence drift in a Matched Filterlike fashion, where intermediate estimates are weighted by their instantaneous SNR. Second, an observed bias is removed by iterating between offset estimation and compensation by resampling a couple of times. The effectiveness of the proposed method is demonstrated by speech recognition results on the output of a beamformer with and without sampling rate offset compensation between the input channels. We compare MVDR and maximum-SNR beamformers in reverberant environments and further show that both benefit from a novel phase normalization, which we also propose in this contribution. author: - first_name: Joerg full_name: Schmalenstroeer, Joerg id: '460' last_name: Schmalenstroeer - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Schmalenstroeer J, Heymann J, Drude L, Boeddeker C, Haeb-Umbach R. Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming. In: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). ; 2017.' apa: Schmalenstroeer, J., Heymann, J., Drude, L., Boeddeker, C., & Haeb-Umbach, R. (2017). Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming. IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). bibtex: '@inproceedings{Schmalenstroeer_Heymann_Drude_Boeddeker_Haeb-Umbach_2017, title={Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming}, booktitle={IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)}, author={Schmalenstroeer, Joerg and Heymann, Jahn and Drude, Lukas and Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2017} }' chicago: Schmalenstroeer, Joerg, Jahn Heymann, Lukas Drude, Christoph Boeddeker, and Reinhold Haeb-Umbach. “Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming.” In IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017. ieee: J. Schmalenstroeer, J. Heymann, L. Drude, C. Boeddeker, and R. Haeb-Umbach, “Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming,” 2017. mla: Schmalenstroeer, Joerg, et al. “Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming.” IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017. short: 'J. Schmalenstroeer, J. Heymann, L. Drude, C. Boeddeker, R. Haeb-Umbach, in: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017.' date_created: 2019-07-12T05:30:20Z date_updated: 2023-10-26T08:12:05Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_SchHaeb.pdf oa: '1' publication: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP) quality_controlled: '1' related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_SchHaeb_poster.pdf status: public title: Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming type: conference user_id: '460' year: '2017' ... --- _id: '11744' 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. author: - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Chinaev A, Heymann J, Drude L, Haeb-Umbach R. Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs. In: 12. ITG Fachtagung Sprachkommunikation (ITG 2016). ; 2016.' apa: Chinaev, A., Heymann, J., Drude, L., & Haeb-Umbach, R. (2016). Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs. In 12. ITG Fachtagung Sprachkommunikation (ITG 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} }' chicago: Chinaev, Aleksej, Jahn Heymann, Lukas Drude, and Reinhold Haeb-Umbach. “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” In 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016. ieee: A. Chinaev, J. Heymann, L. Drude, and R. Haeb-Umbach, “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs,” in 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016. mla: Chinaev, Aleksej, et al. “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016. short: 'A. Chinaev, J. Heymann, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.' date_created: 2019-07-12T05:27:25Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeyDrHa16.pdf oa: '1' publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016) related_material: link: - description: Presentation relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeyDrHa16_Presentation.pdf status: public title: Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs type: conference user_id: '44006' year: '2016' ... --- _id: '11751' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Boeddeker C, Haeb-Umbach R. Blind Speech Separation based on Complex Spherical k-Mode Clustering. In: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). ; 2016.' apa: Drude, L., Boeddeker, C., & Haeb-Umbach, R. (2016). Blind Speech Separation based on Complex Spherical k-Mode Clustering. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). 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} }' chicago: Drude, Lukas, Christoph Boeddeker, and Reinhold Haeb-Umbach. “Blind Speech Separation Based on Complex Spherical K-Mode Clustering.” In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016. ieee: L. Drude, C. Boeddeker, and R. Haeb-Umbach, “Blind Speech Separation based on Complex Spherical k-Mode Clustering,” in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016. mla: Drude, Lukas, et al. “Blind Speech Separation Based on Complex Spherical K-Mode Clustering.” Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016. short: 'L. Drude, C. Boeddeker, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016.' date_created: 2019-07-12T05:27:33Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_drude_paper.pdf oa: '1' publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) related_material: link: - description: Slides relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_drude_slides.pdf status: public title: Blind Speech Separation based on Complex Spherical k-Mode Clustering type: conference user_id: '44006' year: '2016' ... --- _id: '11756' abstract: - lang: eng 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. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Bhiksha full_name: Raj, Bhiksha last_name: Raj - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Raj B, Haeb-Umbach R. On the appropriateness of complex-valued neural networks for speech enhancement. In: INTERSPEECH 2016, San Francisco, USA. ; 2016.' apa: Drude, L., Raj, B., & Haeb-Umbach, R. (2016). On the appropriateness of complex-valued neural networks for speech enhancement. In INTERSPEECH 2016, San Francisco, USA. 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} }' chicago: Drude, Lukas, Bhiksha Raj, and Reinhold Haeb-Umbach. “On the Appropriateness of Complex-Valued Neural Networks for Speech Enhancement.” In INTERSPEECH 2016, San Francisco, USA, 2016. ieee: L. Drude, B. Raj, and R. Haeb-Umbach, “On the appropriateness of complex-valued neural networks for speech enhancement,” in INTERSPEECH 2016, San Francisco, USA, 2016. mla: Drude, Lukas, et al. “On the Appropriateness of Complex-Valued Neural Networks for Speech Enhancement.” INTERSPEECH 2016, San Francisco, USA, 2016. short: 'L. Drude, B. Raj, R. Haeb-Umbach, in: INTERSPEECH 2016, San Francisco, USA, 2016.' date_created: 2019-07-12T05:27:39Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2016/interspeech_2016_drude_paper.pdf oa: '1' publication: INTERSPEECH 2016, San Francisco, USA related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2016/interspeech_2016_drude_slides.pdf status: public title: On the appropriateness of complex-valued neural networks for speech enhancement type: conference user_id: '44006' year: '2016' ... --- _id: '11771' abstract: - lang: eng 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. author: - first_name: Thomas full_name: Glarner, Thomas id: '14169' last_name: Glarner - first_name: Mohammad full_name: Mahdi Momenzadeh, Mohammad last_name: Mahdi Momenzadeh - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Glarner T, Mahdi Momenzadeh M, Drude L, Haeb-Umbach R. Factor Graph Decoding for Speech Presence Probability Estimation. In: 12. ITG Fachtagung Sprachkommunikation (ITG 2016). ; 2016.' apa: Glarner, T., Mahdi Momenzadeh, M., Drude, L., & Haeb-Umbach, R. (2016). Factor Graph Decoding for Speech Presence Probability Estimation. In 12. ITG Fachtagung Sprachkommunikation (ITG 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} }' chicago: Glarner, Thomas, Mohammad Mahdi Momenzadeh, Lukas Drude, and Reinhold Haeb-Umbach. “Factor Graph Decoding for Speech Presence Probability Estimation.” In 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016. ieee: T. Glarner, M. Mahdi Momenzadeh, L. Drude, and R. Haeb-Umbach, “Factor Graph Decoding for Speech Presence Probability Estimation,” in 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016. mla: Glarner, Thomas, et al. “Factor Graph Decoding for Speech Presence Probability Estimation.” 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016. short: 'T. Glarner, M. Mahdi Momenzadeh, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.' date_created: 2019-07-12T05:27:56Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2016/itgspeech2016_08_Glarner.pdf oa: '1' 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 status: public title: Factor Graph Decoding for Speech Presence Probability Estimation type: conference user_id: '44006' year: '2016' ... --- _id: '11812' author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Heymann J, Drude L, Haeb-Umbach R. Neural Network Based Spectral Mask Estimation for Acoustic Beamforming. In: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). ; 2016.' apa: Heymann, J., Drude, L., & Haeb-Umbach, R. (2016). Neural Network Based Spectral Mask Estimation for Acoustic Beamforming. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). 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} }' chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Neural Network Based Spectral Mask Estimation for Acoustic Beamforming.” In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016. ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “Neural Network Based Spectral Mask Estimation for Acoustic Beamforming,” in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016. mla: Heymann, Jahn, et al. “Neural Network Based Spectral Mask Estimation for Acoustic Beamforming.” Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016. short: 'J. Heymann, L. Drude, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016.' date_created: 2019-07-12T05:28:44Z date_updated: 2022-01-06T06:51:09Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_heymann_paper.pdf oa: '1' publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) related_material: link: - description: Slides relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_heymann_slides.pdf status: public title: Neural Network Based Spectral Mask Estimation for Acoustic Beamforming type: conference user_id: '44006' year: '2016' ... --- _id: '11834' 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. author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Heymann J, Drude L, Haeb-Umbach R. Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition. In: Computer Speech and Language. ; 2016.' apa: Heymann, J., Drude, L., & Haeb-Umbach, R. (2016). Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition. In Computer Speech and Language. 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} }' chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition.” In Computer Speech and Language, 2016. ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition,” in Computer Speech and Language, 2016. mla: Heymann, Jahn, et al. “Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition.” Computer Speech and Language, 2016. short: 'J. Heymann, L. Drude, R. Haeb-Umbach, in: Computer Speech and Language, 2016.' date_created: 2019-07-12T05:29:09Z date_updated: 2022-01-06T06:51:11Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2016/chime4_upbonly_paper.pdf oa: '1' publication: Computer Speech and Language related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2016/chime4_upbonly_poster.pdf status: public title: Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition type: conference user_id: '44006' year: '2016' ... --- _id: '11908' abstract: - lang: eng text: 'This paper describes automatic speech recognition (ASR) systems developed jointly by RWTH, UPB and FORTH for the 1ch, 2ch and 6ch track of the 4th CHiME Challenge. In the 2ch and 6ch tracks the final system output is obtained by a Confusion Network Combination (CNC) of multiple systems. The Acoustic Model (AM) is a deep neural network based on Bidirectional Long Short-Term Memory (BLSTM) units. The systems differ by front ends and training sets used for the acoustic training. The model for the 1ch track is trained without any preprocessing. For each front end we trained and evaluated individual acoustic models. We compare the ASR performance of different beamforming approaches: a conventional superdirective beamformer [1] and an MVDR beamformer as in [2], where the steering vector is estimated based on [3]. Furthermore we evaluated a BLSTM supported Generalized Eigenvalue beamformer using NN-GEV [4]. The back end is implemented using RWTH?s open-source toolkits RASR [5], RETURNN [6] and rwthlm [7]. We rescore lattices with a Long Short-Term Memory (LSTM) based language model. The overall best results are obtained by a system combination that includes the lattices from the system of UPB?s submission [8]. Our final submission scored second in each of the three tracks of the 4th CHiME Challenge.' author: - first_name: Tobias full_name: Menne, Tobias last_name: Menne - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Anastasios full_name: Alexandridis, Anastasios last_name: Alexandridis - first_name: Kazuki full_name: Irie, Kazuki last_name: Irie - first_name: Albert full_name: Zeyer, Albert last_name: Zeyer - first_name: Markus full_name: Kitza, Markus last_name: Kitza - first_name: Pavel full_name: Golik, Pavel last_name: Golik - first_name: Ilia full_name: Kulikov, Ilia last_name: Kulikov - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Ralf full_name: Schlüter, Ralf last_name: Schlüter - first_name: Hermann full_name: Ney, Hermann last_name: Ney - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Athanasios full_name: Mouchtaris, Athanasios last_name: Mouchtaris citation: ama: 'Menne T, Heymann J, Alexandridis A, et al. The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation. In: Computer Speech and Language. ; 2016.' apa: Menne, T., Heymann, J., Alexandridis, A., Irie, K., Zeyer, A., Kitza, M., … Mouchtaris, A. (2016). The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation. In Computer Speech and Language. bibtex: '@inproceedings{Menne_Heymann_Alexandridis_Irie_Zeyer_Kitza_Golik_Kulikov_Drude_Schlüter_et al._2016, title={The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation}, booktitle={Computer Speech and Language}, author={Menne, Tobias and Heymann, Jahn and Alexandridis, Anastasios and Irie, Kazuki and Zeyer, Albert and Kitza, Markus and Golik, Pavel and Kulikov, Ilia and Drude, Lukas and Schlüter, Ralf and et al.}, year={2016} }' chicago: Menne, Tobias, Jahn Heymann, Anastasios Alexandridis, Kazuki Irie, Albert Zeyer, Markus Kitza, Pavel Golik, et al. “The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation.” In Computer Speech and Language, 2016. ieee: T. Menne et al., “The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation,” in Computer Speech and Language, 2016. mla: Menne, Tobias, et al. “The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation.” Computer Speech and Language, 2016. short: 'T. Menne, J. Heymann, A. Alexandridis, K. Irie, A. Zeyer, M. Kitza, P. Golik, I. Kulikov, L. Drude, R. Schlüter, H. Ney, R. Haeb-Umbach, A. Mouchtaris, in: Computer Speech and Language, 2016.' date_created: 2019-07-12T05:30:35Z date_updated: 2022-01-06T06:51:12Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2016/chime4_rwthupbforth_paper.pdf oa: '1' publication: Computer Speech and Language status: public title: The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation type: conference user_id: '44006' year: '2016' ... --- _id: '11755' abstract: - lang: eng text: This contribution presents a Direction of Arrival (DoA) estimation algorithm based on the complex Watson distribution to incorporate both phase and level differences of captured micro- phone array signals. The derived algorithm is reviewed in the context of the Generalized State Coherence Transform (GSCT) on the one hand and a kernel density estimation method on the other hand. A thorough simulative evaluation yields insight into parameter selection and provides details on the performance for both directional and omni-directional microphones. A comparison to the well known Steered Response Power with Phase Transform (SRP-PHAT) algorithm and a state of the art DoA estimator which explicitly accounts for aliasing, shows in particular the advantages of presented algorithm if inter-sensor level differences are indicative of the DoA, as with directional microphones. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Florian full_name: Jacob, Florian last_name: Jacob - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Jacob F, Haeb-Umbach R. DOA-Estimation based on a Complex Watson Kernel Method. In: 23th European Signal Processing Conference (EUSIPCO 2015). ; 2015.' apa: Drude, L., Jacob, F., & Haeb-Umbach, R. (2015). DOA-Estimation based on a Complex Watson Kernel Method. In 23th European Signal Processing Conference (EUSIPCO 2015). bibtex: '@inproceedings{Drude_Jacob_Haeb-Umbach_2015, title={DOA-Estimation based on a Complex Watson Kernel Method}, booktitle={23th European Signal Processing Conference (EUSIPCO 2015)}, author={Drude, Lukas and Jacob, Florian and Haeb-Umbach, Reinhold}, year={2015} }' chicago: Drude, Lukas, Florian Jacob, and Reinhold Haeb-Umbach. “DOA-Estimation Based on a Complex Watson Kernel Method.” In 23th European Signal Processing Conference (EUSIPCO 2015), 2015. ieee: L. Drude, F. Jacob, and R. Haeb-Umbach, “DOA-Estimation based on a Complex Watson Kernel Method,” in 23th European Signal Processing Conference (EUSIPCO 2015), 2015. mla: Drude, Lukas, et al. “DOA-Estimation Based on a Complex Watson Kernel Method.” 23th European Signal Processing Conference (EUSIPCO 2015), 2015. short: 'L. Drude, F. Jacob, R. Haeb-Umbach, in: 23th European Signal Processing Conference (EUSIPCO 2015), 2015.' date_created: 2019-07-12T05:27:38Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2015/DrJaHa15.pdf oa: '1' publication: 23th European Signal Processing Conference (EUSIPCO 2015) related_material: link: - description: Presentation relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2015/DrJaHa15_Presentation.pdf status: public title: DOA-Estimation based on a Complex Watson Kernel Method type: conference user_id: '44006' year: '2015' ... --- _id: '11810' author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Heymann J, Drude L, Chinaev A, Haeb-Umbach R. BLSTM supported GEV Beamformer Front-End for the 3RD CHiME Challenge. In: Automatic Speech Recognition and Understanding Workshop (ASRU 2015). ; 2015.' apa: Heymann, J., Drude, L., Chinaev, A., & Haeb-Umbach, R. (2015). BLSTM supported GEV Beamformer Front-End for the 3RD CHiME Challenge. In Automatic Speech Recognition and Understanding Workshop (ASRU 2015). bibtex: '@inproceedings{Heymann_Drude_Chinaev_Haeb-Umbach_2015, title={BLSTM supported GEV Beamformer Front-End for the 3RD CHiME Challenge}, booktitle={Automatic Speech Recognition and Understanding Workshop (ASRU 2015)}, author={Heymann, Jahn and Drude, Lukas and Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2015} }' chicago: Heymann, Jahn, Lukas Drude, Aleksej Chinaev, and Reinhold Haeb-Umbach. “BLSTM Supported GEV Beamformer Front-End for the 3RD CHiME Challenge.” In Automatic Speech Recognition and Understanding Workshop (ASRU 2015), 2015. ieee: J. Heymann, L. Drude, A. Chinaev, and R. Haeb-Umbach, “BLSTM supported GEV Beamformer Front-End for the 3RD CHiME Challenge,” in Automatic Speech Recognition and Understanding Workshop (ASRU 2015), 2015. mla: Heymann, Jahn, et al. “BLSTM Supported GEV Beamformer Front-End for the 3RD CHiME Challenge.” Automatic Speech Recognition and Understanding Workshop (ASRU 2015), 2015. short: 'J. Heymann, L. Drude, A. Chinaev, R. Haeb-Umbach, in: Automatic Speech Recognition and Understanding Workshop (ASRU 2015), 2015.' date_created: 2019-07-12T05:28:41Z date_updated: 2022-01-06T06:51:09Z department: - _id: '54' language: - iso: eng publication: Automatic Speech Recognition and Understanding Workshop (ASRU 2015) status: public title: BLSTM supported GEV Beamformer Front-End for the 3RD CHiME Challenge type: conference user_id: '44006' year: '2015' ... --- _id: '11919' abstract: - lang: eng text: In this paper we present a source counting algorithm to determine the number of speakers in a speech mixture. In our proposed method, we model the histogram of estimated directions of arrival with a nonparametric Bayesian infinite Gaussian mixture model. As an alternative to classical model selection criteria and to avoid specifying the maximum number of mixture components in advance, a Dirichlet process prior is employed over the mixture components. This allows to automatically determine the optimal number of mixture components that most probably model the observations. We demonstrate by experiments that this model outperforms a parametric approach using a finite Gaussian mixture model with a Dirichlet distribution prior over the mixture weights. author: - first_name: Oliver full_name: Walter, Oliver last_name: Walter - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Walter O, Drude L, Haeb-Umbach R. Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture Model. In: 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015). ; 2015.' apa: Walter, O., Drude, L., & Haeb-Umbach, R. (2015). Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture Model. In 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015). bibtex: '@inproceedings{Walter_Drude_Haeb-Umbach_2015, title={Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture Model}, booktitle={40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)}, author={Walter, Oliver and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2015} }' chicago: Walter, Oliver, Lukas Drude, and Reinhold Haeb-Umbach. “Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of an Infinite Gaussian Mixture Model.” In 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015. ieee: O. Walter, L. Drude, and R. Haeb-Umbach, “Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture Model,” in 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015. mla: Walter, Oliver, et al. “Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of an Infinite Gaussian Mixture Model.” 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015. short: 'O. Walter, L. Drude, R. Haeb-Umbach, in: 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015.' date_created: 2019-07-12T05:30:47Z date_updated: 2022-01-06T06:51:12Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2015/WaDrHa15.pdf oa: '1' publication: 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015) related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2015/WaDrHa15_Poster.pdf status: public title: Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture Model type: conference user_id: '44006' year: '2015' ... --- _id: '11752' abstract: - lang: eng text: ' "In this contribution we derive a variational EM (VEM) algorithm for model selection in complex Watson mixture models, which have been recently proposed as a model of the distribution of normalized microphone array signals in the short-time Fourier transform domain. The VEM algorithm is applied to count the number of active sources in a speech mixture by iteratively estimating the mode vectors of the Watson distributions and suppressing the signals from the corresponding directions. A key theoretical contribution is the derivation of the MMSE estimate of a quadratic form involving the mode vector of the Watson distribution. The experimental results demonstrate the effectiveness of the source counting approach at moderately low SNR. It is further shown that the VEM algorithm is more robust w.r.t. used threshold values." ' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Dang Hai full_name: Tran Vu, Dang Hai last_name: Tran Vu - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models. In: 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014). ; 2014.' apa: Drude, L., Chinaev, A., Tran Vu, D. H., & Haeb-Umbach, R. (2014). Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models. In 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014). bibtex: '@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models}, booktitle={39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2014} }' chicago: Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models.” In 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014. ieee: L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models,” in 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014. mla: Drude, Lukas, et al. “Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models.” 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014. short: 'L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.' date_created: 2019-07-12T05:27:34Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHa2014.pdf oa: '1' publication: 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHa2014_Poster.pdf status: public title: Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models type: conference user_id: '44006' year: '2014' ... --- _id: '11753' abstract: - lang: eng text: This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Aleksej full_name: Chinaev, Aleksej last_name: Chinaev - first_name: Dang Hai full_name: Tran Vu, Dang Hai last_name: Tran Vu - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014). ; 2014:213-217.' apa: Drude, L., Chinaev, A., Tran Vu, D. H., & Haeb-Umbach, R. (2014). Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014) (pp. 213–217). bibtex: '@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models}, booktitle={14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2014}, pages={213–217} }' chicago: Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” In 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 213–17, 2014. ieee: L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models,” in 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217. mla: Drude, Lukas, et al. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–17. short: 'L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.' date_created: 2019-07-12T05:27:35Z date_updated: 2022-01-06T06:51:08Z department: - _id: '54' keyword: - Accuracy - Acoustics - Estimation - Mathematical model - Soruce separation - Speech - Vectors - Bayes methods - Blind source separation - Directional statistics - Number of speakers - Speaker diarization language: - iso: eng main_file_link: - open_access: '1' url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14.pdf oa: '1' page: 213-217 publication: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014) related_material: link: - description: Poster relation: supplementary_material url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14_Poster.pdf status: public title: Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models type: conference user_id: '44006' year: '2014' ...