Rautenberg, F., Kuhlmann, M., Ebbers, J., Wiechmann, J., Seebauer, F., Wagner, P., &; Haeb-Umbach, R. (2023). Speech Disentanglement for Analysis and Modification of Acoustic and Perceptual Speaker Characteristics. Fortschritte Der Akustik - DAGA 2023, 1409–1412.

Kuhlmann, M., Seebauer, F., Ebbers, J., Wagner, P., &; Haeb-Umbach, R. (2022). Investigation into Target Speaking Rate Adaptation for Voice Conversion. Interspeech 2022.https://doi.org/10.21437/interspeech.2022-10740 

Ebbers, J., Haeb-Umbach, R., &; Serizel, R. (2022). Threshold Independent Evaluation of Sound Event Detection Scores. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

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

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

Ebbers, J., &; Haeb-Umbach, R. (2020). Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCA).

Gburrek, T., Glarner, T., Ebbers, J., Haeb-Umbach, R., &; Wagner, P. (2019). Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion. Proc. 10th ISCA Speech Synthesis Workshop, 81–86.https://doi.org/10.21437/SSW.2019-15 

Ebbers, J., &; Haeb-Umbach, R. (2019). Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision. DCA Workshop, New York, USA.

Ebbers, J., Drude, L., Haeb-Umbach, R., Brendel, A., &; Kellermann, W. (2019). Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks. CAMSAP 2019, Guadeloupe, West Indies.

Nelus, A., Ebbers, J., Haeb-Umbach, R., &; Martin, R. (2019). Privacy-preserving Variational Information Feature Extraction for Domestic Activity Monitoring Versus Speaker Identification. INTERSPEECH 2019, Graz, Austria.

Ebbers, J., Nelus, A., Martin, R., &; Haeb-Umbach, R. (2018). Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection. In DAGA 2018, München.

Glarner, T., Hanebrink, P., Ebbers, J., &; Haeb-Umbach, R. (2018). Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery. INTERSPEECH 2018, Hyderabad, India.

Ebbers, J., Heitkaemper, J., Schmalenstroeer, J., &; Haeb-Umbach, R. (2018). Benchmarking Neural Network Architectures for Acoustic Sensor Networks. ITG 2018, Oldenburg, Germany.

Ebbers, J., Heymann, J., Drude, L., Glarner, T., Haeb-Umbach, R., &; Raj, B. (2017). Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery. INTERSPEECH 2017, Stockholm, Schweden.