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AVEC'19: Audio/Visual Emotion Challenge and Workshop

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Maja PanticAuthors Info & Claims

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

Pages 2718 - 2719

https://doi.org/10.1145/3343031.3350550

Published: 15 October 2019 Publication History

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Abstract

The ninth Audio-Visual Emotion Challenge and workshop AVEC 2019 was held in conjunction with ACM Multimedia'19. This year, the AVEC series addressed major novelties with three distinct tasks: State-of-Mind Sub-challenge (SoMS), Detecting Depression with Artificial Intelligence Sub-challenge (DDS), and Cross-cultural Emotion Sub-challenge (CES). The SoMS was based on a novel dataset (USoM corpus) that includes self-reported mood (10-point Likert scale) after the narrative of personal stories (two positive and two negative). The DDS was based on a large extension of the DAIC-WOZ corpus (c.f. AVEC 2016) that includes new recordings of patients suffering from depression with the virtual agent conducting the interview being, this time, wholly driven by AI, i.e., without any human intervention. The CES was based on the SEWA dataset (c.f. AVEC 2018) that has been extended with the inclusion of new participants in order to investigate how emotion knowledge of Western European cultures (German, Hungarian) can be transferred to the Chinese culture. In this summary, we mainly describe participation and conditions of the AVEC Challenge.

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Liz-López HKeita MTaleb-Ahmed AHadid AHuertas-Tato JCamacho D(2024)Generation and detection of manipulated multimodal audiovisual content: Advances, trends and open challengesInformation Fusion10.1016/j.inffus.2023.102103103(102103)Online publication date: Mar-2024
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Mamontov DZepf SKarpov AMinker W(2024)Cross-Cultural Automatic Depression Detection Based on Audio SignalsSpeech and Computer10.1007/978-3-031-77961-9_23(309-323)Online publication date: 25-Nov-2024
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Index Terms

AVEC'19: Audio/Visual Emotion Challenge and Workshop
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance

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Information & Contributors

Information

Published In

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

October 2019

2794 pages

ISBN:9781450368896

DOI:10.1145/3343031

General Chairs:
Laurent Amsaleg
CNRS-IRISA, France
,
Benoit Huet
EURECOM, France
,
Martha Larson
Radboud University and TU Delft (Netherlands)
,
Program Chairs:
Guillaume Gravier
CNRS-IRISA, France
,
Hayley Hung
Delft University of Technology Netherlands
,
Chong-Wah Ngo
City University of Hong Kong Hong Kong
,
Wei Tsang Ooi
National University of Singapore Singapore

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2019

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MM '19

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SIGMM

MM '19: The 27th ACM International Conference on Multimedia

October 21 - 25, 2019

Nice, France

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MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Firoz NBerestneva OAksyonov S(2024)Dual Layer Cogni - Insight Deep-Mood Encoder: A Two- Tiered Approach for Depression Detection2024 International Russian Smart Industry Conference (SmartIndustryCon)10.1109/SmartIndustryCon61328.2024.10516113(928-937)Online publication date: 25-Mar-2024
https://doi.org/10.1109/SmartIndustryCon61328.2024.10516113
Liz-López HKeita MTaleb-Ahmed AHadid AHuertas-Tato JCamacho D(2024)Generation and detection of manipulated multimodal audiovisual content: Advances, trends and open challengesInformation Fusion10.1016/j.inffus.2023.102103103(102103)Online publication date: Mar-2024
https://doi.org/10.1016/j.inffus.2023.102103
Mamontov DZepf SKarpov AMinker W(2024)Cross-Cultural Automatic Depression Detection Based on Audio SignalsSpeech and Computer10.1007/978-3-031-77961-9_23(309-323)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77961-9_23
Firoz NBeresteneva OAksyonov S(2023)Enhancing Depression Detection: Employing Autoencoders and Linguistic Feature Analysis with BERT and LSTM Model2023 International Russian Automation Conference (RusAutoCon)10.1109/RusAutoCon58002.2023.10272795(299-304)Online publication date: 10-Sep-2023
https://doi.org/10.1109/RusAutoCon58002.2023.10272795
Gupta RSinha R(2023)On the Automatic Detection of Comorbidity of Mental Disorders Using Audio-Visual Data2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)10.1109/REEDCON57544.2023.10151164(118-122)Online publication date: 1-May-2023
https://doi.org/10.1109/REEDCON57544.2023.10151164
Gupta RSinha R(2023)An Investigation on the Audio-Video Data Based Estimation of Emotion Regulation Difficulties and Their Association With Mental DisordersIEEE Access10.1109/ACCESS.2023.329709111(74324-74336)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3297091
Wu YMao KDennett LZhang YChen J(2023)Systematic review of machine learning in PTSD studies for automated diagnosis evaluationnpj Mental Health Research10.1038/s44184-023-00035-w2:1Online publication date: 27-Sep-2023
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Verde LCampanile LMarulli FMarrone S(2022)Speech-based Evaluation of Emotions-Depression Correlation2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927758(1-6)Online publication date: 12-Sep-2022
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Hosler BSalvi DMurray AAntonacci FBestagini PTubaro SStamm M(2021)Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW53098.2021.00112(1013-1022)Online publication date: Jun-2021
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Ray AKumar SReddy RMukherjee PGarg RRingeval FSchuller BValstar MCummins NCowie RPantic M(2019)Multi-level Attention Network using Text, Audio and Video for Depression PredictionProceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop10.1145/3347320.3357697(81-88)Online publication date: 15-Oct-2019
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