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 .
LibreCat: /record/33806
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 .
LibreCat: /record/33958
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 .
LibreCat: /record/33819
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.
LibreCat: /record/33847
arXiv: 2209.11494

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.
LibreCat: /record/33848
arXiv: 2111.07578

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 .
LibreCat: /record/33807
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 .
LibreCat: /record/33451
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.
LibreCat: /record/49113

J. Wiechmann, T. Glarner, F. Rautenberg, P. Wagner, and R. Haeb-Umbach, “Technically enabled explaining of voice characteristics,” Bielefeld, 2022.
LibreCat: /record/33696

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 .
LibreCat: /record/33857
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 .
LibreCat: /record/33808
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.
LibreCat: /record/33816
DOI: 10.48550/ARXIV.2205.00944

J. Ebbers, R. Haeb-Umbach, and R. Serizel, “Threshold Independent Evaluation of Sound Event Detection Scores,” 2022.
LibreCat: /record/34072

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.
LibreCat: /record/21065
DOI: 10.1109/JPROC.2020.3018668

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 .
LibreCat: /record/28256
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 .
LibreCat: /record/28262
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 .
LibreCat: /record/28261
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.
LibreCat: /record/24000

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.
LibreCat: /record/44843
arXiv: 2106.05627

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 .
LibreCat: /record/28259
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.
LibreCat: /record/23998

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 .
LibreCat: /record/22528
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 .
LibreCat: /record/23994
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.
LibreCat: /record/23999

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.
LibreCat: /record/23997

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.
LibreCat: /record/29304

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 .
LibreCat: /record/26770
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.
LibreCat: /record/29173

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.
LibreCat: /record/29308

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.
LibreCat: /record/29306

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 .
LibreCat: /record/24456
DOI: 10.1109/tcds.2020.3044366

R. Haeb-Umbach, “Sprachtechnologien für Digitale Assistenten,” in Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020, 2020, pp. 227–234.
LibreCat: /record/17763

C. Boeddeker, T. Nakatani, K. Kinoshita, and R. Haeb-Umbach, “Jointly Optimal Dereverberation and Beamforming,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.
LibreCat: /record/20695
DOI: 10.1109/icassp40776.2020.9054393

C. Boeddeker et al., “Towards a speaker diarization system for the CHiME 2020 dinner party transcription,” in Proc. CHiME 2020 Workshop on Speech Processing in Everyday Environments, 2020.
LibreCat: /record/20700

T. Nakatani, C. Boeddeker, K. Kinoshita, R. Ikeshita, M. Delcroix, and R. Haeb-Umbach, “Jointly optimal denoising, dereverberation, and source separation,”10.1109/TASLP.2020.3013118__IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 1–1, 2020, doi:10.1109/TASLP.2020.3013118 .
LibreCat: /record/17598
DOI: 10.1109/TASLP.2020.3013118

J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, and R. Haeb-Umbach, “Demystifying TasNet: A Dissecting Approach,” 2020.
LibreCat: /record/20504

S. Watanabe et al., “CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for  Unsegmented Recordings,” arXiv:2004.09249. 2020.
LibreCat: /record/28263

J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments,” 2020.
LibreCat: /record/20505

T. von Neumann et al., “End-to-End Training of Time Domain Audio Separation and Recognition,”10.1109/ICASSP40776.2020.9053461__in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 7004–7008, doi:10.1109/ICASSP40776.2020.9053461 .
LibreCat: /record/20762
DOI: 10.1109/ICASSP40776.2020.9053461

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 .
LibreCat: /record/20764
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.
LibreCat: /record/18651

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 .
LibreCat: /record/20766
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.
LibreCat: /record/20753

R. Haeb-Umbach, “Lektionen für Alexa \'26; Co?!,” forschung, vol. 44, no. 1, pp. 12–15, 2019.
LibreCat: /record/17762
DOI: 10.1002/fors.201970104

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.
LibreCat: /record/19446

L. Drude, J. Heymann, and R. Haeb-Umbach, “Unsupervised training of neural mask-based beamforming,” in INTERSPEECH 2019, Graz, Austria, 2019.
LibreCat: /record/11965

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.
LibreCat: /record/12874

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.
LibreCat: /record/12875

G. Kurz et al., “Directional Statistics and Filtering Using libDirectional,” in Journal of Statistical Software 89(4), 2019.
LibreCat: /record/12876

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