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.
LibreCat: /record/44849

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

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

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

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

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

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 .
LibreCat: /record/15237
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.
LibreCat: /record/15794

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.
LibreCat: /record/15796

A. Nelus, J. Ebbers, R. Haeb-Umbach, and R. Martin, “Privacy-preserving Variational Information Feature Extraction for Domestic Activity Monitoring Versus Speaker Identification,” 2019.
LibreCat: /record/15792

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.
LibreCat: /record/11760

T. Glarner, P. Hanebrink, J. Ebbers, and R. Haeb-Umbach, “Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2018.
LibreCat: /record/11907

J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Benchmarking Neural Network Architectures for Acoustic Sensor Networks,” 2018.
LibreCat: /record/11836

J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, and B. Raj, “Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2017.
LibreCat: /record/11759