T. Cord-Landwehr, T. von Neumann, C. Boeddeker, and R. Haeb-Umbach, “MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator,” presented at the 2022 International Workshop on Acoustic Signal Enhancement (IWAENC), Bamberg, 2022.

T. Cord-Landwehr, C. Boeddeker, T. von Neumann, C. Zorila, R. Doddipatla, and R. Haeb-Umbach, “Monaural source separation: From anechoic to reverberant environments,” presented at the 2022 International Workshop on Acoustic Signal Enhancement (IWAENC), 2022.

T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-Varying Sampling Rate Offsets and Speaker Changes,”10.1109/icassp43922.2022.9746284__2022, doi:10.1109/icassp43922.2022.9746284 .

C. Grimm, T. Fei, E. Warsitz, R. Farhoud, T. Breddermann, and R. Haeb-Umbach, “Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications,”10.1109/TVT.2022.3182411__IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9435–9449, 2022, doi:10.1109/TVT.2022.3182411 .

J. Wiechmann, T. Glarner, F. Rautenberg, P. Wagner, and R. Haeb-Umbach, “Technically enabled explaining of voice characteristics,” Bielefeld, 2022.

M. Kuhlmann, F. Seebauer, J. Ebbers, P. Wagner, and R. Haeb-Umbach, “Investigation into Target Speaking Rate Adaptation for Voice Conversion,”10.21437/interspeech.2022-10740__2022, doi:10.21437/interspeech.2022-10740 .

T. Gburrek, J. Schmalenstroeer, J. Heitkaemper, and R. Haeb-Umbach, “Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription,”10.1109/IWAENC53105.2022.9914772__presented at the 17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022),10.1109/IWAENC53105.2022.9914772__Bamberg, Germany , 2022, doi:10.1109/IWAENC53105.2022.9914772 .

T. Gburrek, C. Boeddeker, T. von Neumann, T. Cord-Landwehr, J. Schmalenstroeer, and R. Haeb-Umbach, A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network. arXiv, 2022.

J. Ebbers, R. Haeb-Umbach, and R. Serizel, “Threshold Independent Evaluation of Sound Event Detection Scores,” 2022.

R. Haeb-Umbach, J. Heymann, L. Drude, S. Watanabe, M. Delcroix, and T. Nakatani, “Far-Field Automatic Speech Recognition,” Proceedings of the IEEE, vol. 109, no. 2, pp. 124–148, 2021.

W. Zhang et al., “End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend,”10.1109/icassp39728.2021.9414464__2021, doi:10.1109/icassp39728.2021.9414464 .

J. Heitkaemper, J. Schmalenstroeer, V. Ion, and R. Haeb-Umbach, “A Database for Research on Detection and Enhancement of Speech Transmitted over HF links,” in Speech Communication; 14th ITG-Symposium, 2021, pp. 1–5.

C. Boeddeker, F. Rautenberg, and R. Haeb-Umbach, “A Comparison and Combination of Unsupervised Blind Source Separation  Techniques,” presented at the ITG Conference on Speech Communication, Kiel, 2021.

C. Boeddeker et al., “Convolutive Transfer Function Invariant SDR Training Criteria for Multi-Channel Reverberant Speech Separation,”10.1109/icassp39728.2021.9414661__2021, doi:10.1109/icassp39728.2021.9414661 .

J. Schmalenstroeer, J. Heitkaemper, J. Ullmann, and R. Haeb-Umbach, “Open Range Pitch Tracking for Carrier Frequency Difference Estimation from HF Transmitted Speech,” in 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 1–5.

T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Geometry calibration in wireless acoustic sensor networks utilizing DoA and distance information,”10.1186/s13636-021-00210-x__EURASIP Journal on Audio, Speech, and Music Processing, 2021, doi:10.1186/s13636-021-00210-x .

T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks,”10.1109/icassp39728.2021.9413831__2021, doi:10.1109/icassp39728.2021.9413831 .

T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “On Source-Microphone Distance Estimation Using Convolutional Recurrent Neural Networks,” in Speech Communication; 14th ITG-Symposium, 2021, pp. 1–5.

J. Ebbers, M. Kuhlmann, T. Cord-Landwehr, and R. Haeb-Umbach, “Contrastive Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised Learning of Disentangled Speech Representations,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 3860–3864.

T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers,”10.21437/interspeech.2021-1177__presented at the Interspeech, 2021, doi:10.21437/interspeech.2021-1177 .