Artificial

Intelligence

Lab

For additional information and material about our publications,
visit https://bib.sebastianstober.de

2023

• Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion
  Suhita Ghosh*, Arnab Das*, Yamini Sinha, Ingo Siegert, Tim Polzehl and Sebastian Stober 
  In: Interspeech 2023 - Dublin, Ireland, 20-24 August 2023
  [URL] [Github]
  
  
• StarGAN-VC++: Towards Emotion Preserving Voice Conversion Using Deep Embeddings.
  Arnab Das*, Suhita Ghosh*, Tim Polzehl and Sebastian Stober
  In: 12th Speech Synthesis Workshop (SSW) 2023 - Grenoble, France, 26-30 August 2023
  [URL] [Github]
  
  
• Anonymization of Stuttered Speech – Removing Speaker Information while Preserving the Utterance.
  Jan Hintz*, Sebastian Bayerl*, Yamini Sinha, Suhita Ghosh, Martha Schubert, Sebastian Stober, Korbinian Riedhammer and Ingo Siegert
  In: 3rd Symposium on Security and Privacy in Speech Communication - Dublin, Ireland, 19 August 2023
  [URL]
  
  
• Visualizing Deep Neural Networks with Topographic Activation Maps.
  Valerie Krug; Raihan Kabir Ratul; Christopher Olson and Sebastian Stober.
  In: HHAI 2023: Augmenting Human Intellect. IOS Press, 2023. 138-152.
  [URL] [Github]
  
  

2022

• Voice Privacy Leveraging Multi-Scale Blocks with ECAPA-TDNN SE-Res2NeXT Extension for Speaker Anonymization.
  Razieh Khamsehashari, Yamini Sinha, Jan Hintz, Suhita Ghosh, Tim Polzehl, Carlos Franzreb, Sebastian Stober and Ingo Siegert.
  In: 2nd Symposium on Security and Privacy in Speech Communication - Incheon, Korea, 23-24 September 2022 - International Speech Communication Association. https://doi.org/10.21437/spsc.2022-8

• Projektseminar „Künstliche Intelligenz in den Neurowissenschaften – interdisziplinäre und anwendungsnahe Lehre umsetzen“.
  Johannes Schleiss, Robert Brockhoff und Sebastian Stober.
  In: Mah, D.-K., & Torner, C. (Hrsg.) (2022): Anwendungsorientierte Hochschullehre zu Künstlicher Intelligenz. Impulse aus dem Fellowship-Programm zur Integration von KI-Campus-Lernangeboten. Berlin: KI-Campus. https://doi.org/10.5281/zenodo.7319832
  

• Rahmenbedingungen für Künstliche Intelligenz in Educational Technology.
  Johannes Schleiss und Stefan Göllner.
  IN: Proceedings of DELFI Workshops 2022 - Die 20. Fachtagung Bildungstechnologien, 2022.
  [URL]
  
  

• An Interdisciplinary Competence Profile for AI in Engineering.
  Johannes Schleiss; Michelle Ines Bieber; Anke Manukjan; Lars Kellner & Sebastian Stober.
  In: Proceedings of the 50th European Society for Engineering Education (SEFI) Anual Conference, 2022.
  [URL]
  
  
• Teaching AI Competencies in Engineering using Projects and Open Educational Resources.
  Johannes Schleiss; Julia Hense; Andreas Kist; Jörn Schlingensiepen & Sebastian Stober
  In: Proceedings of the 50th European Society for Engineering Education (SEFI) Anual Conference, 2022.
  [URL]
  
  

• Towards Patient Specific Reconstruction Using Perception-Aware CNN and Planning CT as Prior.
  Suhita Ghosh; Philipp Ernst; Georg Rose; Andreas Nürnberger and Sebastian Stober.
  In: IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022.
  [URL]
  
  
• Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss.
  Philipp Ernst, Suhita Ghosh, Georg Rose, Andreas Nürnberger.
  Medical Imaging with Deep Learning, MIDL 2022, Zürich, Switzerland, July 06, 2022, Medical Imaging with Deep Learning
  [URL]
  
  
• Protecting Student Data in ML Pipelines: An Overview of Privacy-Preserving ML.
  Johannes Schleiss; Kolja Günther & Sebastian Stober.
  In: Proceedings of the International Conference on Artificial Intelligence in Education, 532-536, 2022.
  [URL]
  
  
• BiTe-REx: An Explainable Bilingual Text Retrieval System in the Automotive Domain.
  Viju Sudhi; Sabine Wehnert; Norbert Michael Homner; Sebastian Ernst; Mark Gonter; Andreas Krug & Ernesto William De Luca.
  In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pages 3251–3255, 2022.
  [URL]
  
  

• Visualizing Deep Neural Networks with Topographic Activation Maps.
  Andreas Krug; Raihan Kabir Ratul & Sebastian Stober.
  In: arXiv preprint arXiv:2204.03528, 2022.
  [PDF] [github]

2021

• Hierarchical Predictive Coding and Interpretable Audio Analysis-Synthesis.
  André Ofner; Johannes Schleiss & Sebastian Stober.
  In: Proc. of the 15th International Symposium on CMMR, 2021.
  [PDF]
  
  
• Uncertainty-aware temporal self-learning (UATS) - semi-supervised learning for segmentation of prostate zones and beyond.
  Anneke Meyer; Suhita Ghosh; Daniel Schindele; Martin Schostak; Sebastian Stober; Christian Hansen & Marko Rak.
  In: Artificial intelligence in medicine: AIM - Amsterdam [u.a.]: Elsevier Science - AIM, Bd. 116, 2021.
  [PDF]
  
  
• Analyzing and Visualizing Deep Neural Networks for Speech Recognition with Saliency-Adjusted Neuron Activation Profiles.
  Andreas Krug; Maral Ebrahimzadeh; Jost Alemann; Jens Johannsmeier & Sebastian Stober.
  In: Electronics 10 (11), 1350, 2021.
  [URL]
  
  
• Perceptual Losses Facilitate CT Denoising and Artifact Removal.
  Suhita Ghosh; Andreas Krug; Georg Rose & Sebastian Stober.
  In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS). IEEE, 2021.
  [URL]
  
  

• Distributed Planning with Active Inference.
  André Ofner; Johannes Schleiss & Sebastian Stober.
  In: Bernstein Conference, 2021.
  [URL]
  
  
• Visualizing Artificial Neural Network Activations as Topographic Maps.
  Andreas Krug; Raihan Kabir Ratul & Sebastian Stober.
  In: Bernstein Conference, 2021.
  [URL]
  
  
• Few-Shot Bioacoustic Event Detection via Segmentation using Prototypical Networks.
  Jens Johannsmeier & Sebastian Stober.
  In: Detection and Classification of Acoustic Scenes and Events (DCASE), 2021.
  [PDF]
  
  
• Approaching Scheduling Problems via a Deep Hybrid Greedy Model and Supervised Learning.
  Johann Schmidt & Sebastian Stober.
  In: IFAC-PapersOnline 54 (1), 805-810, 2021.
  [URL]

2020

• Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net.
  Nader Aldoj; Federico Biavati; Florian Michallek; Sebastian Stober & Marc Dewey.
  In: Scientific Reports, Volume 10, Number 1, Springer Science and Business Media LLC, 2020.
  [DOI] [PDF]
  
  
• Balancing Active Inference and Active Learning with Deep Variational Predictive Coding for EEG.
  André Ofner & Sebastian Stober.
  In: IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020), 2020.
  [URL]
  
  
• Modeling perception with hierarchical prediction: Auditory segmentation with deep predictive coding locates candidate evoked potentials in EEG.
  André Ofner & Sebastian Stober.
  In: ResearchGATE: scientific neetwork ; the leading professional network for scientists - Cambridge, Mass.: ResearchGATE Corp., 2010, 2020.
  [URL] [PDF]
  
  
• Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.
  Amirali Vahid; Moritz Mückschel; Sebastian Stober: Ann-Kathrin Stock & Christian Beste.
  In: Communications biology - London: Springer Nature, Vol. 3.2020, Art.-Nr. 112, 11, 2020.
  [DOI] [PDF]
  
  
• Analyzing regions of safety for handling shared data in cooperative systems.
  Georg Jäger; Johannes Schleiss; Sasiporn Usanavasin; Sebastian Stober & Sebastian Zug.
  In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020.
  [URL]
  
  
• PredNet and Predictive Coding: A Critical Review.
  Roshan Prakash Rane; Edit Szügyi; Vageesh Saxena; André Ofner & Sebastian Stober.
  In: Proceedings of the 2020 International Conference on Multimedia Retrieval, ICMR ’20, Pages 233–241, Association for Computing Machinery, New York, NY, USA, 2020.
  [DOI] [PDF]
  
  

• Exploration of interpretability techniques for deep COVID-19 classification using chest X-ray images.
  Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck & Andreas Nürnberger.
  In: arXiv preprint arXiv:2006.02570, 2020.
  [PDF]
  
  
• Gradient-adjusted neuron activation profiles for comprehensive introspection of convolutional speech recognition models.
  Andreas Krug & Sebastian Stober.
  In: arXiv preprint arXiv:2002.08125, 2020.
  [PDF]

2019

• Window-Based Neural Tagging for Shallow Discourse Argument Labeling.
  René Knaebel; Manfred Stede & Sebastian Stober.
  In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Pages 768-777, 2019.
  [DOI] [PDF]
  
  
• The ISMIR Explorer – A Visual Interface for Exploring 20 Years of ISMIR Publications.
  Thomas Low; Christian Hentschel; Sayantan Polley; Anustup Das; Harald Sack; Andreas Nürnberger & Sebastian Stober.
  In: 20th International Society for Music Information Retrieval Conference (ISMIR’19), Pages 392-399, 2019.
  [PDF]
  
  
• Predictive Coding Based Vision For Autonomous Cars.
  Roshan Prakash Rane;​ André Ofner; Shreyas Gite​ & Sebastian Stober.
  In: Computational Cognition 2019 Workshop, 2019.
  [URL]
  
  
• Knowledge transfer in coupled predictive coding networks.
  André Ofner & Sebastian Stober.
  In: Bernstein Conference, 2019.
  [DOI]
  
  
• Visualizing Deep Neural Networks for Speech Recognition with Learned Topographic Filter Maps.
  Andreas Krug & Sebastian Stober.
  In: Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2019.
  [PDF]
  
  
• Siri visualisiert.
  Andreas Krug & Sebastian Stober.
  In: Proceedings of the 2019 NaWik Symposium Karlsruhe, Pages 24-25, 2019.
  
  
• Deep Learning Based on Event-Related EEG Differentiates Children with ADHD from Healthy Controls.
  Amirali Vahid; Annet Bluschke; Veit Roessner; Sebastian Stober & Christian Beste.
  In: Journal of Clinical Medicine, Volume 8, Number 7, 2019.
  [DOI] [PDF]
  
  
• Hybrid Variational Predictive Coding as a Bridge between Human and Artificial Cognition.
  André Ofner & Sebastian Stober.
  In: The 2019 Conference on Artificial Life, Number 31, Pages 68-69, 2019.
  [URL] [PDF]
  
  
• Automatic prostate and prostate zones segmentation of magnetic resonance images using convolutional neural networks.
  Nader Aldoj; Federico Biavati; Miriam Rutz; Florian Michallek; Sebastian Stober & Marc Dewey.
  In: Proceedings of International Conference on Medical Imaging with Deep Learning (MIDL’19), 2019.
  [DOI] [PDF]