Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source Counting, Separation and ASR
T. von Neumann, C. Boeddeker, L. Drude, K. Kinoshita, M. Delcroix, T. Nakatani, R. Haeb-Umbach, in: Proc. Interspeech 2020, 2020, pp. 3097–3101.

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

Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation
K. Kinoshita, T. von Neumann, M. Delcroix, T. Nakatani, R. Haeb-Umbach, in: Proc. Interspeech 2020, 2020, pp. 2652–2656.

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

Lektionen für Alexa \'26 Co?!
R. Haeb-Umbach, Forschung 44 (2019) 12–15.

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

Unsupervised training of neural mask-based beamforming
L. Drude, J. Heymann, R. Haeb-Umbach, in: INTERSPEECH 2019, Graz, Austria, 2019.

Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation
L. Drude, D. Hasenklever, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK, 2019.

Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR
J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: ICASSP 2019, Brighton, UK, 2019.

Directional Statistics and Filtering Using libDirectional
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.