AVEC 2013: the continuous audio/visual emotion and depression recognition challenge

By Michel François Valstar, Björn Schuller, Kirsty Smith, Florian Eyben, Bihan Jiang, Sanjay Bilakhia, Sebastian Schnieder, R. Cowie, Maja Pantic. AVEC@ACM Multimedia, 2013

619 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/9884de41a7cadc0bb41a2c21b3ffb4dc89610e03

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017, background): “Nevertheless, AVEC uses only data captured in controlled conditions and under very specific scenarios.”
Deep learning in mental health outcome research: a scoping review (2020, background): “…to elicit facial expressions and speech responses of the subjects of bipolar disorder, unipolar depression, and healthy controls; 72 and (ii) the International Audio/Visual Emotion Recognition Challenges (AVEC) depression dataset 73 – 75 , collected within human – computer interaction scenario.”
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, background): “Audio-Visual Emotion recognition Challenge (AVEC) series of competitions [36], [37], [38], [39], [40], [41] pro-”
Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018, methodology): “[62] reports the best performance of by using the large AVEC-2013 feature set [65] on the EMO-DB dataset, and the large ComParE feature set [66] on the eNTERFACE05 dataset.”
AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge (2014, background): “depression corpus [27], which is formed of 150 videos of taskoriented depression data recorded in a human-computer interaction scenario.”
EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “In the ield of neurology, identiied emotions in response to speciic stimuli and the corresponding neural activities can be analyzed for diagnosing some afective disorders, such as PTSD (post straumatic stress disorder) [7] and depression [8, 9].”
A review of depression and suicide risk assessment using speech analysis (2015, background): “Overfitting is a potential reason for the discrepancy between the development and test sets baselines in the 2013 Audio/Visual Emotion Challenge baseline paper (Valstar et al., 2013), Section 7.”
Multimodal Machine Learning: A Survey and Taxonomy (2017, background): “later instantiation including healthcare applications such as automatic assessment of depression and anxiety [208].”
Technological innovations in mental healthcare: harnessing the digital revolution (2015, background): “6 correspond closely with clinical ratings of depression [4] and could potentially augment”
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition (2015, methodology): “The Audio/Visual Emotion Challenges (AVEC) [126], [127], [158] evaluated dimensional affect models.”
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2018, methodology): “It is interesting to investigate the performance of our proposed method on dimensional emotion recognition tasks, such as the recognition of depression degree on the AVDLC dataset [67].”