T. von Neumann et al., “Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source Counting, Separation and ASR,”10.21437/Interspeech.2020-2519__in Proc. Interspeech 2020, 2020, pp. 3097–3101, doi:10.21437/Interspeech.2020-2519 .

T. Gburrek, J. Schmalenstroeer, A. Brendel, W. Kellermann, and R. Haeb-Umbach, “Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Network,” 2020.

K. Kinoshita, T. von Neumann, M. Delcroix, T. Nakatani, and R. Haeb-Umbach, “Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation,”10.21437/Interspeech.2020-2388__in Proc. Interspeech 2020, 2020, pp. 2652–2656, doi:10.21437/Interspeech.2020-2388 .

J. Ebbers and R. Haeb-Umbach, “Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection,” 2020.

R. Haeb-Umbach, “Lektionen für Alexa \'26; Co?!,” forschung, vol. 44, no. 1, pp. 12–15, 2019.

L. Drude, J. Heitkaemper, C. Boeddeker, and R. Haeb-Umbach, “SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition,” ArXiv e-prints, 2019.

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

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.

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.

G. Kurz et al., “Directional Statistics and Filtering Using libDirectional,” in Journal of Statistical Software 89(4), 2019.

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.

J. Heymann and B. L. Khe Chai Sim, “Improving CTC Using Stimulated Learning for Sequence Modeling,” in ICASSP 2019, Brighton, UK, 2019.

C. Zorila, C. Boeddeker, R. Doddipatla, and R. Haeb-Umbach, “An Investigation Into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party Transcription,” in ASRU 2019, Sentosa, Singapore, 2019.

J. Heitkaemper, T. Feher, M. Freitag, and R. Haeb-Umbach, “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, 2019.

J. M. Martin-Donas, J. Heitkaemper, R. Haeb-Umbach, A. M. Gomez, and A. M. Peinado, “Multi-Channel Block-Online Source Extraction based on Utterance Adaptation,” in INTERSPEECH 2019, Graz, Austria, 2019.

N. Kanda, C. Boeddeker, J. Heitkaemper, Y. Fujita, S. Horiguchi, and R. Haeb-Umbach, “Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR,” in INTERSPEECH 2019, Graz, Austria, 2019.

T. von Neumann, K. Kinoshita, M. Delcroix, S. Araki, T. Nakatani, and R. Haeb-Umbach, “All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis,” in ICASSP 2019, Brighton, UK, 2019.

R. Haeb-Umbach et al., “Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques,”10.1109/MSP.2019.2918706__IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 111–124, 2019, doi:10.1109/MSP.2019.2918706 .

R. Haeb-Umbach, “Lektionen für Alexa &; Co?!,”10.1002/fors.201970104__DFG forschung 1/2019, pp. 12–15, 2019, doi:10.1002/fors.201970104 .

T. Gburrek, T. Glarner, J. Ebbers, R. Haeb-Umbach, and P. Wagner, “Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion,”10.21437/SSW.2019-15__in Proc. 10th ISCA Speech Synthesis Workshop, Vienna, 2019, pp. 81–86, doi:10.21437/SSW.2019-15 .

J. Ebbers and R. Haeb-Umbach, “Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision,” 2019.

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.

A. Nelus, J. Ebbers, R. Haeb-Umbach, and R. Martin, “Privacy-preserving Variational Information Feature Extraction for Domestic Activity Monitoring Versus Speaker Identification,” 2019.

J. Heymann, M. Bacchiani, and T. N. Sainath, “Performance of Mask Based Statistical Beamforming in a Smart Home Scenario,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 6722–6726.

J. Ebbers, A. Nelus, R. Martin, and R. Haeb-Umbach, “Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection,” in DAGA 2018, München, 2018.

J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Frame-Online DNN-WPE Dereverberation,” in IWAENC 2018, Tokio, Japan, 2018.

J. Heitkaemper, J. Heymann, and R. Haeb-Umbach, “Smoothing along Frequency in Online Neural Network Supported Acoustic Beamforming,” in ITG 2018, Oldenburg, Germany, 2018.

L. Drude et al., “Integration neural network based beamforming and weighted prediction error dereverberation,” in INTERSPEECH 2018, Hyderabad, India, 2018.

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.

V. Despotovic, O. Walter, and R. Haeb-Umbach, “Machine learning techniques for semantic analysis of dysarthric speech: An experimental study,” Speech Communication 99 (2018) 242-251 (Elsevier B.V.), 2018.

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.

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.

C. Boeddeker, H. Erdogan, T. Yoshioka, and R. Haeb-Umbach, “Exploring Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition,” in ICASSP 2018, Calgary, Canada, 2018.

S. Watanabe et al., “ESPnet: End-to-End Speech Processing Toolkit,”10.21437/Interspeech.2018-1456__in INTERSPEECH 2018, Hyderabad, India, 2018, pp. 2207–2211, doi:10.21437/Interspeech.2018-1456 .

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.

H. Afifi, J. Schmalenstroeer, J. Ullmann, R. Haeb-Umbach, and H. Karl, “MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks,” in Speech Communication; 13th ITG-Symposium, 2018, pp. 1–5.

C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, and R. Haeb-Umbach, “Discrimination of Stationary from Moving Targets with Recurrent Neural Networks in Automotive Radar,” 2018.

T. Glarner, P. Hanebrink, J. Ebbers, and R. Haeb-Umbach, “Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2018.

J. Schmalenstroeer and R. Haeb-Umbach, “Efficient Sampling Rate Offset Compensation - An Overlap-Save Based Approach,” 2018.

M. Kitza et al., “The RWTH/UPB System Combination for the CHiME 2018 Workshop,” 2018.

J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Benchmarking Neural Network Architectures for Acoustic Sensor Networks,” 2018.

J. Schmalenstroeer and R. Haeb-Umbach, “Insights into the Interplay of Sampling Rate Offsets and MVDR Beamforming,” 2018.

P. Arora and R. Haeb-Umbach, “A Study on Transfer Learning for Acoustic Event Detection in a Real Life Scenario,” in IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017.

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.

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.

A. Chinaev and R. Haeb-Umbach, “A Generalized Log-Spectral Amplitude Estimator for Single-Channel Speech Enhancement,” in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.

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.

T. Glarner, B. Boenninghoff, O. Walter, and R. Haeb-Umbach, “Leveraging Text Data for Word Segmentation for Underresourced Languages,” in INTERSPEECH 2017, Stockholm, Schweden, 2017.

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.

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.