Continuous emotion detection in response to music videos

Mohammad Soleymani · Sander Koelstra · Ioannis Patras · Thierry Pun

84 citations0/0 files storedInternational Conference on Automatic Face and Gesture Recognition

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Emotion and Mood Recognition Heart Rate Variability and Autonomic Control Music and Audio Processing

Abstract

Viewers' preference for multimedia selection depends highly on their emotional experience. In this paper, we present an emotion detection method for music videos using central and peripheral nervous system physiological signals as well as multimedia content analysis. A set of 40 music clips eliciting a broad range of emotions were first selected. After extracting the one minute long emotional highlight of each video, they were shown to 32 participants while their physiological responses were recorded. Participants self-reported their felt emotions after watching each clip by means of arousal, valence, dominance, and liking ratings. The physiological signals included electroencephalogram, galvanic skin response, respiration pattern, skin temperature, electromyograms and blood volume pulse using plethysmograph. Emotional features were extracted from the signals and the multimedia content. The emotional features were used to train a linear ridge regressor to detect emotions for each participant using a leave-one-out cross-validation strategy. The performance of the personalized emotion detection is shown to be significantly superior to a random regressor.

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References

1997Affective Computing 2002Praat, a system for doing phonetics by computer 1977Evidence for a three-factor theory of emotions 1991Culture and the categorization of emotions.2007The World of Emotions is not Two-Dimensional 1994Effects of color on emotions.2008Emotion recognition based on physiological changes in music listening 2013The Effects of Color on Emotions

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2017Toolbox for Emotional feAture extraction from Physiological signals (TEAP)2018EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences 2013Hybrid video emotional tagging using users’ EEG and video content 2017Continuous, Real-Time Emotion Annotation: A Novel Joystick-Based Analysis Framework 2019The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features 2017Affective Recognition in Dynamic and Interactive Virtual Environments

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