C. Boeddeker, A. S. Subramanian, G. Wichern, R. Haeb-Umbach, and J. Le Roux, “TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings,”10.1109/taslp.2024.3350887__IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 1185–1197, 2024, doi:10.1109/taslp.2024.3350887 .

T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “On the Integration of Sampling Rate Synchronization and Acoustic Beamforming,” presented at the European Signal Processing Conference (EUSIPCO), Helsinki, 2023.

T. Cord-Landwehr, C. Boeddeker, C. Zoril, R. Doddipatla, and R. Haeb-Umbach, “Frame-Wise and Overlap-Robust Speaker Embeddings for Meeting Diarization,”10.1109/icassp49357.2023.10095370__presented at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes, 2023, doi:10.1109/icassp49357.2023.10095370 .

J. Schmalenstroeer, T. Gburrek, and R. Haeb-Umbach, “LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices,” presented at the ITG Conference on Speech Communication, Aachen, 2023.

T. Cord-Landwehr, C. Boeddeker, C. Zoril, R. Doddipatla, and R. Haeb-Umbach, “A Teacher-Student Approach for Extracting Informative Speaker Embeddings From Speech Mixtures,”10.21437/interspeech.2023-1379__2023, doi:10.21437/interspeech.2023-1379 .

F. Rautenberg, M. Kuhlmann, J. Wiechmann, F. Seebauer, P. Wagner, and R. Haeb-Umbach, “On Feature Importance and Interpretability of Speaker Representations,” presented at the ITG Conference on Speech Communication, Aachen, 2023.

J. Wiechmann, F. Rautenberg, P. Wagner, and R. Haeb-Umbach, “Explaining voice characteristics to novice voice practitioners-How successful is it?,” 2023.

R. Aralikatti, C. Boeddeker, G. Wichern, A. Subramanian, and J. Le Roux, “Reverberation as Supervision For Speech Separation,”10.1109/icassp49357.2023.10095022__2023, doi:10.1109/icassp49357.2023.10095022 .

S. Berger, P. Vieting, C. Boeddeker, R. Schlüter, and R. Haeb-Umbach, “Mixture Encoder for Joint Speech Separation and Recognition,”10.21437/interspeech.2023-1815__2023, doi:10.21437/interspeech.2023-1815 .

F. Seebauer, M. Kuhlmann, R. Haeb-Umbach, and P. Wagner, “Re-examining the quality dimensions of synthetic speech,” 2023.

T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria,”10.1109/taslp.2022.3228629__IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 576–589, 2023, doi:10.1109/taslp.2022.3228629 .

T. von Neumann, C. Boeddeker, K. Kinoshita, M. Delcroix, and R. Haeb-Umbach, “On Word Error Rate Definitions and Their Efficient Computation for Multi-Speaker Speech Recognition Systems,”10.1109/icassp49357.2023.10094784__2023, doi:10.1109/icassp49357.2023.10094784 .

T. von Neumann, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems,” presented at the CHiME 2023 Workshop on Speech Processing in Everyday Environments, Dublin, 2023.

T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks,” presented at the 57th Asilomar Conference on Signals, Systems, and Computers, 2023.

J. Ebbers, R. Haeb-Umbach, and R. Serizel, “Post-Processing Independent Evaluation of Sound Event Detection Systems,” in Proceedings of the 8th Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCA), 2023, pp. 36–40.

F. Rautenberg et al., “Speech Disentanglement for Analysis and Modification of Acoustic and Perceptual Speaker Characteristics,” in Fortschritte der Akustik - DAGA 2023, Hamburg, 2023, pp. 1409–1412.

W. Zhang, X. Chang, C. Boeddeker, T. Nakatani, S. Watanabe, and Y. Qian, “End-to-End Dereverberation, Beamforming, and Speech Recognition in A Cocktail Party,”10.1109/TASLP.2022.3209942__IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022, doi:10.1109/TASLP.2022.3209942 .

C. Boeddeker, T. Cord-Landwehr, T. von Neumann, and R. Haeb-Umbach, “An Initialization Scheme for Meeting Separation with Spatial Mixture Models,”10.21437/interspeech.2022-10929__2022, doi:10.21437/interspeech.2022-10929 .

J. Heitkämper, J. Schmalenstroeer, and R. Haeb-Umbach, “Neural Network Based Carrier Frequency Offset Estimation From Speech Transmitted Over High Frequency Channels,” presented at the 30th European Signal Processing Conference (EUSIPCO), Belgrad.

H. Afifi, H. Karl, T. Gburrek, and J. Schmalenstroeer, “Data-driven Time Synchronization in Wireless Multimedia Networks,”10.1109/iwcmc55113.2022.9824980__2022, doi:10.1109/iwcmc55113.2022.9824980 .

K. Kinoshita, T. von Neumann, M. Delcroix, C. Boeddeker, and R. Haeb-Umbach, “Utterance-by-utterance overlap-aware neural diarization with Graph-PIT,”10.21437/Interspeech.2022-11408__in Proc. Interspeech 2022, 2022, pp. 1486–1490, doi:10.21437/Interspeech.2022-11408 .

T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “SA-SDR: A Novel Loss Function for Separation of Meeting Style Data,”10.1109/icassp43922.2022.9746757__2022, doi:10.1109/icassp43922.2022.9746757 .

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. Ebbers and R. Haeb-Umbach, Pre-Training And Self-Training For Sound Event Detection In Domestic Environments. 2022.

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 .

C. Li et al., “ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for ASR Integration,”10.1109/slt48900.2021.9383615__2021, doi:10.1109/slt48900.2021.9383615 .

C. Li et al., “Dual-Path RNN for Long Recording Speech Separation,”10.1109/slt48900.2021.9383514__2021, doi:10.1109/slt48900.2021.9383514 .

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.

A. Chinaev, G. Enzner, T. Gburrek, and J. Schmalenstroeer, “Online Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks with Packet Loss,” in 29th European Signal Processing Conference (EUSIPCO), 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 .

T. von Neumann, C. Boeddeker, K. Kinoshita, M. Delcroix, and R. Haeb-Umbach, “Speeding Up Permutation Invariant Training for Source Separation,” presented at the Speech Communication; 14th ITG Conference, Kiel, 2021.

J. Ebbers and R. Haeb-Umbach, “Self-Trained Audio Tagging and Sound Event Detection in Domestic Environments,” in Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCA), 2021, pp. 226–230.

J. Ebbers, M. C. Keyser, and R. Haeb-Umbach, “Adapting Sound Recognition to A New Environment Via Self-Training,” in Proceedings of the 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 1135–1139.

K. J. Rohlfing et al., “Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems,”10.1109/tcds.2020.3044366__IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 717–728, 2021, doi:10.1109/tcds.2020.3044366 .