- Sponsor:
- sigmm
It is our great pleasure to welcome you to the 6th Audio-Visual Emotion recognition Challenge -- Depression, Mood, and Emotion (AVEC 2016), held in conjunction with the ACM Multimedia 2016 in Amsterdam, The Netherlands. This year's challenge and associated workshop continues to push the boundaries of audio-visual emotion recognition, and sees a return to the Behaviomedical topic of automatic depression recognition. The first and second AVEC challenges posed the problem of detecting emotions on an extremely large set of natural audio-visual behaviour data. In its third and fourth editions, we extended the problem even further to include the prediction of self-reported severity of depression, which is a frequently occurring mood disorder. The fifth edition included physiological data for emotion prediction. This year we see sub-challenges on two datasets -- RECOLA that was used also in AVEC 2015 and the newly introduced DAIC-WOZ dataset. It is our intention to make it a tradition to repeat each sub-challenge for a second year, while also introducing a new sub-challenge. We hope this encourages more participants to take part and will increase interaction between researchers from closely related research areas.
The mission of AVEC challenge and workshop series is to provide a common benchmark test set for individual multimodal information processing and to bring together the audio and video emotion recognition communities, to compare the relative merits of the two approaches to emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. A second motivation is the need to advance emotion recognition systems to be able to deal with naturalistic behaviour in large volumes of unsegmented, non-prototypical and non-preselected. As you will see, these goals have been reached with the selection of this year's data and the challenge contributions.
The call for participation attracted 14 submissions from Asia, Europe, and North America. The programme committee accepted 12 papers in addition to the baseline paper for oral presentation. For the depression sub-challenge we received submissions by 7 teams, and for the emotion subchallenge a record 13 teams submitted results! We hope that these proceedings will serve as a valuable reference for researchers and developers in the area of audio-visual emotion recognition and depression analysis.
We also encourage attendees to attend the keynote presentations. This valuable and insightful talk can and will guide us to a better understanding of the state of the field, and future directions:
Personality and Emotion Analysis, Dr Hatice Gunes (University of Cambridge, UK)
Proceeding Downloads
Multimodal Analysis of Impressions and Personality in Human-Computer and Human-Robot Interactions
This talk will focus on automatic prediction of impressions and inferences about traits and characteristics of people based on their multimodal observable behaviours in the context of human-virtual character and human-robot interactions. The first part ...
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge
- Michel Valstar,
- Jonathan Gratch,
- Björn Schuller,
- Fabien Ringeval,
- Denis Lalanne,
- Mercedes Torres Torres,
- Stefan Scherer,
- Giota Stratou,
- Roddy Cowie,
- Maja Pantic
The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological ...
Detecting Depression using Vocal, Facial and Semantic Communication Cues
- James R. Williamson,
- Elizabeth Godoy,
- Miriam Cha,
- Adrianne Schwarzentruber,
- Pooya Khorrami,
- Youngjune Gwon,
- Hsiang-Tsung Kung,
- Charlie Dagli,
- Thomas F. Quatieri
Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via ...
Staircase Regression in OA RVM, Data Selection and Gender Dependency in AVEC 2016
- Zhaocheng Huang,
- Brian Stasak,
- Ting Dang,
- Kalani Wataraka Gamage,
- Phu Le,
- Vidhyasaharan Sethu,
- Julien Epps
Within the field of affective computing, human emotion and disorder/disease recognition have progressively attracted more interest in multimodal analysis. This submission to the Depression Classification and Continuous Emotion Prediction challenges for ...
Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text
- Anastasia Pampouchidou,
- Olympia Simantiraki,
- Amir Fazlollahi,
- Matthew Pediaditis,
- Dimitris Manousos,
- Alexandros Roniotis,
- Georgios Giannakakis,
- Fabrice Meriaudeau,
- Panagiotis Simos,
- Kostas Marias,
- Fan Yang,
- Manolis Tsiknakis
Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities ...
DepAudioNet: An Efficient Deep Model for Audio based Depression Classification
This paper presents a novel and effective audio based method on depression classification. It focuses on two important issues, \emph{i.e.} data representation and sample imbalance, which are not well addressed in literature. For the former one, in ...
Multimodal and Multiresolution Depression Detection from Speech and Facial Landmark Features
- Md Nasir,
- Arindam Jati,
- Prashanth Gurunath Shivakumar,
- Sandeep Nallan Chakravarthula,
- Panayiotis Georgiou
Automatic classification of depression using audiovisual cues can help towards its objective diagnosis. In this paper, we present a multimodal depression classification system as a part of the 2016 Audio/Visual Emotion Challenge and Workshop (AVEC2016). ...
High-Level Geometry-based Features of Video Modality for Emotion Prediction
The automatic analysis of emotion remains a challenging task in unconstrained experimental conditions. In this paper, we present our contribution to the 6th Audio/Visual Emotion Challenge (AVEC 2016), which aims at predicting the continuous emotional ...
Online Affect Tracking with Multimodal Kalman Filters
Arousal and valence have been widely used to represent emotions dimensionally and measure them continuously in time. In this paper, we introduce a computational framework for tracking these affective dimensions from multimodal data as an entry to the ...
Continuous Multimodal Human Affect Estimation using Echo State Networks
A continuous multimodal human affect recognition for both arousal and valence dimensions in a non-acted spontaneous scenario is investigated in this paper. Different regression models based on Random Forests and Echo State Networks are evaluated and ...
Multimodal Emotion Recognition for AVEC 2016 Challenge
- Filip Povolny,
- Pavel Matejka,
- Michal Hradis,
- Anna Popková,
- Lubomir Otrusina,
- Pavel Smrz,
- Ian Wood,
- Cecile Robin,
- Lori Lamel
This paper describes a systems for emotion recognition and its application on the dataset from the AV+EC 2016 Emotion Recognition Challenge. The realized system was produced and submitted to the AV+EC 2016 evaluation, making use of all three modalities (...
Exploring Multimodal Visual Features for Continuous Affect Recognition
This paper presents our work in the Emotion Sub-Challenge of the 6th Audio/Visual Emotion Challenge and Workshop (AVEC 2016), whose goal is to explore utilizing audio, visual and physiological signals to continuously predict the value of the emotion ...
Decision Tree Based Depression Classification from Audio Video and Language Information
In order to improve the recognition accuracy of the Depression Classification Sub-Challenge (DCC) of the AVEC 2016, in this paper we propose a decision tree for depression classification. The decision tree is constructed according to the distribution of ...
Multi-Modal Audio, Video and Physiological Sensor Learning for Continuous Emotion Prediction
- Kevin Brady,
- Youngjune Gwon,
- Pooya Khorrami,
- Elizabeth Godoy,
- William Campbell,
- Charlie Dagli,
- Thomas S. Huang
The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. The Audio Video Emotion ...
Cited By
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Liang L, Wang Y, Ma H, Zhang R, Liu R, Zhu R, Zheng Z, Zhang X and Wang F (2024). Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning, Frontiers in Psychiatry, 10.3389/fpsyt.2024.1422020, 15
- Li M, Cao L, Zhai Q, Li P, Liu S, Li R, Feng L, Wang G, Hu B, Lu S and Gelfusa M (2020). Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement, Complexity, 2020, Online publication date: 1-Jan-2020.
- Song S, Shen L and Valstar M Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), (158-165)
Index Terms
- Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge