J
Jarek Krajewski
Researcher at University of Wuppertal
Publications - 78
Citations - 2690
Jarek Krajewski is an academic researcher from University of Wuppertal. The author has contributed to research in topics: Support vector machine & Feature extraction. The author has an hindex of 21, co-authored 73 publications receiving 2093 citations. Previous affiliations of Jarek Krajewski include University of Cologne & University of Würzburg.
Papers
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Journal ArticleDOI
A review of depression and suicide risk assessment using speech analysis
Nicholas Cummins,Stefan Scherer,Jarek Krajewski,Sebastian Schnieder,Julien Epps,Thomas F. Quatieri +5 more
TL;DR: How common paralinguistic speech characteristics are affected by depression and suicidality and the application of this information in classification and prediction systems is reviewed.
Proceedings ArticleDOI
AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge
Michel Valstar,Björn Schuller,Kirsty Smith,Timur Almaev,Florian Eyben,Jarek Krajewski,Roddy Cowie,Maja Pantic +7 more
TL;DR: The fourth Audio-Visual Emotion recognition Challenge (AVEC 2014) is presented, using a subset of the tasks used in a previous challenge, allowing for more focussed studies and the performance of the baseline system on the two tasks is presented.
Proceedings Article
The INTERSPEECH 2011 Speaker State Challenge
TL;DR: The INTERSPEECH 2011 Speaker State Challenge addresses two new sub-challenges to overcome the usually low compatibility of results: in the Intoxication Sub-Challenge, alcoholisation of speakers has to be determined in two classes; in the Sleepy Language Corpus, another two-class classification task has to been solved.
Proceedings ArticleDOI
The INTERSPEECH 2017 computational paralinguistics challenge: Addressee, cold & snoring
Björn Schuller,Björn Schuller,Stefan Steidl,Anton Batliner,Anton Batliner,Elika Bergelson,Jarek Krajewski,Christoph Janott,Andrei Amatuni,Marisa Casillas,Amdanda Seidl,Melanie Soderstrom,Anne S. Warlaumont,Guillermo Hidalgo,Sebastian Schnieder,Clemens Heiser,Winifried Hohenhorst,Michael Herzog,Maximilian Schmitt,Kun Qian,Yue Zhang,Yue Zhang,George Trigeorgis,Panagiotis Tzirakis,Stefanos Zafeiriou,Stefanos Zafeiriou +25 more
TL;DR: These sub-challenges, their conditions, and the baseline feature extraction and classifiers are described, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audiowords for the first time in the challenge series.
Proceedings ArticleDOI
Steering Wheel Behavior Based Estimation of Fatigue
TL;DR: This paper examined a steering behavior based fatigue monitoring system using advanced signal processing procedures for feature extraction to capture fatigue impaired steering patterns and yielded a recognition rate of 86.1% in classifying slight from strong fatigue.