Gburrek, T., Schmalenstroeer, J., Brendel, A., Kellermann, W., &; Haeb-Umbach, R. (2020). Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Network. European Signal Processing Conference (EUSIPCO).
LibreCat: /record/18651

Kinoshita, K., von Neumann, T., Delcroix, M., Nakatani, T., &; Haeb-Umbach, R. (2020). Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation. Proc. Interspeech 2020, 2652–2656.https://doi.org/10.21437/Interspeech.2020-2388 
LibreCat: /record/20766
DOI: 10.21437/Interspeech.2020-2388

Ebbers, J., &; Haeb-Umbach, R. (2020). Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCA).
LibreCat: /record/20753

Haeb-Umbach, R. (2019). Lektionen für Alexa \'26; Co?! Forschung, 44(1), 12–15.https://doi.org/10.1002/fors.201970104 
LibreCat: /record/17762
DOI: 10.1002/fors.201970104

Drude, L., Heitkaemper, J., Boeddeker, C., &; Haeb-Umbach, R. (2019). SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition. ArXiv E-Prints.
LibreCat: /record/19446

Drude, L., Heymann, J., &; Haeb-Umbach, R. (2019). Unsupervised training of neural mask-based beamforming. In INTERSPEECH 2019, Graz, Austria.
LibreCat: /record/11965

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.
LibreCat: /record/12874

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.
LibreCat: /record/12875

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).
LibreCat: /record/12876

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 
LibreCat: /record/12890
DOI: 10.1109/JSTSP.2019.2912565

Heymann, J., &; Khe Chai Sim, B. L. (2019). Improving CTC Using Stimulated Learning for Sequence Modeling. In ICASSP 2019, Brighton, UK.
LibreCat: /record/15812

Zorila, C., Boeddeker, C., Doddipatla, R., &; Haeb-Umbach, R. (2019). An Investigation Into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party Transcription. In ASRU 2019, Sentosa, Singapore.
LibreCat: /record/15816

Heitkaemper, J., Feher, T., Freitag, M., &; Haeb-Umbach, R. (2019). A Study on Online Source Extraction in the Presence of Changing Speaker Positions. In International Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia.
LibreCat: /record/14822

Martin-Donas, J. M., Heitkaemper, J., Haeb-Umbach, R., Gomez, A. M., &; Peinado, A. M. (2019). Multi-Channel Block-Online Source Extraction based on Utterance Adaptation. In INTERSPEECH 2019, Graz, Austria.
LibreCat: /record/14824

Kanda, N., Boeddeker, C., Heitkaemper, J., Fujita, Y., Horiguchi, S., &; Haeb-Umbach, R. (2019). Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR. In INTERSPEECH 2019, Graz, Austria.
LibreCat: /record/14826

von Neumann, T., Kinoshita, K., Delcroix, M., Araki, S., Nakatani, T., &; Haeb-Umbach, R. (2019). All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis. In ICASSP 2019, Brighton, UK.
LibreCat: /record/13271

Haeb-Umbach, R., Watanabe, S., Nakatani, T., Bacchiani, M., Hoffmeister, B., Seltzer, M. L., Zen, H., &; Souden, M. (2019). Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques. IEEE Signal Processing Magazine, 36(6), 111–124.https://doi.org/10.1109/MSP.2019.2918706 
LibreCat: /record/15814
DOI: 10.1109/MSP.2019.2918706

Haeb-Umbach, R. (2019). Lektionen für Alexa &; Co?! DFG Forschung 1/2019, 12–15.https://doi.org/10.1002/fors.201970104 
LibreCat: /record/19450
DOI: 10.1002/fors.201970104

Gburrek, T., Glarner, T., Ebbers, J., Haeb-Umbach, R., &; Wagner, P. (2019). Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion. Proc. 10th ISCA Speech Synthesis Workshop, 81–86.https://doi.org/10.21437/SSW.2019-15 
LibreCat: /record/15237
DOI: 10.21437/SSW.2019-15

Ebbers, J., &; Haeb-Umbach, R. (2019). Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision. DCA Workshop, New York, USA.
LibreCat: /record/15794