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Sebastian Möller

Researcher at Technical University of Berlin

Publications -  531
Citations -  7103

Sebastian Möller is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Quality (business) & Quality of experience. The author has an hindex of 34, co-authored 491 publications receiving 5830 citations. Previous affiliations of Sebastian Möller include German Research Centre for Artificial Intelligence & University of Oslo.

Papers
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Proceedings Article

Experiences with and new application ideas for the interspeech app.

TL;DR: Interspeech 2013 as mentioned in this paper presented a mobile phone application that supports conference participants prior to and during the conference, and demonstrated usage experiences collected during the 2013 and 2014 Interspeak conferences, and propose and implement ideas for carrying out speech research with such an app.
Proceedings Article

Linguistically Motivated Evaluation of the 2022 State-of-the-art Machine Translation Systems for Three Language Directions

TL;DR: In this article , a fine-grained linguistically motivated analysis of 29 machine translation systems submitted at the shared task of the 7th Conference of Machine Translation (WMT22) is presented.
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Corrigendum: Real-time affect detection in virtual reality: A technique based on a three-dimensional model of affect and EEG signals

TL;DR: Quality and Usability Lab, Institute for Software Technology and Theoretical Computer Science, Faculty of Electrical Engineering and Computer Science (IETC), Technische Universitat Berlin, Berlin, Germany, UTS Games Studio, University of Technology Sydney UTS, Sydney, NSW, Australia, German Research Center for Artificial Intelligence (DFKI), Berlin,Germany, Hamm-Lippstadt University of Applied Sciences, Hamm, Germany as discussed by the authors
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How can we model a quality event? Some considerations on describing quality judgment and prediction processes

TL;DR: In this article, the authors introduce a systemic view on a listener in an auditory experiment, which helps to separate sound events from auditory events and from their descriptions, and to identify and describe the processes involved in such experiments.
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Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision

TL;DR: In this article , a deep learning semantic segmentation model with U-net architecture was proposed to identify the femoral nerve in ultrasound images. And the results showed a mean intersection over Union of 74%, with an interquartile range of 0.66-0.81.