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audEERING's approach to the One-Minute-Gradual Emotion Challenge

Computer Vision and Pattern Recognition 2018-05-04 v1 Machine Learning Sound Audio and Speech Processing Machine Learning
Authors: Andreas Triantafyllopoulos , Hesam Sagha , Florian Eyben , Björn Schuller
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Abstract

This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We provide the results for audio and video processing on subject (in)dependent evaluations. On the provided Development set, we achieved 0.343 Concordance Correlation Coefficient (CCC) for arousal (from audio) and .401 for valence (from video).

Keywords

emotion recognition OMG challenge audio processing video processing concordance correlation coefficient

Cite

@article{arxiv.1805.01222,
  title  = {audEERING's approach to the One-Minute-Gradual Emotion Challenge},
  author = {Andreas Triantafyllopoulos and Hesam Sagha and Florian Eyben and Björn Schuller},
  journal= {arXiv preprint arXiv:1805.01222},
  year   = {2018}
}

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