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Leveraging recent advances in deep learning for audio-Visual emotion recognition · Study

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ArticleLeveraging recent advances in deep learning for audio-Visual emotion recognition

Liam Schoneveld2021

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Articlegreen2021

Leveraging recent advances in deep learning for audio-Visual emotion recognition

Liam Schoneveld · Alice Othmani · Hazem Abdelkawy

238 citations·0/0 files stored·Pattern Recognition Letters

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Emotion and Mood Recognition Speech and Audio Processing Face and Expression Recognition

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References

1989A Concordance Correlation Coefficient to Evaluate Reproducibility 2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild 2017A review of affective computing: From unimodal analysis to multimodal fusion 2019The Handbook of Culture and Psychology 2013Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions 2017End-to-End Multimodal Emotion Recognition Using Deep Neural Networks 1997A Note On "A Concordance Correlation Coefficient to Evaluate Reproducibility"

Cited by

2023A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations 2022Automated emotion recognition: Current trends and future perspectives 2022Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild 2023A study on computer vision for facial emotion recognition 2021Multimodal Engagement Analysis From Facial Videos in the Classroom

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Details

Year2021

Typearticle

Published2021-03-13

VenuePattern Recognition Letters

ISSN-L0167-8655

Languageen

Citations238

Files0 public / 0

ShelfEmotion and Mood Recognition

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doi10.1016/j.patrec.2021.03.007

openalex_workW3137028092

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2023Speech emotion recognition using machine learning — A systematic review

2022A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition

2023Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review