Remarks on computational emotion classification from physiological signal - Evaluation of how jazz music chord progression influences emotion

Abstract

This paper evaluates human emotional change by sound stimuli focused on chord progression in jazz music and conducts computational emotion classification from physiological information. Psychological experiments using chord progression tunes as sound stimuli are conducted with 117 subjects and the result of subjective evaluation shows that positive emotional valance chord progression tunes that have ascending fourth aroused positive images, and negative emotional valence chord progression tunes that have chromatic descent aroused negative images. Psychophysical experiments using chord progression tunes to excite emotions in subjects are conducted to gather acceleration plethysmogram data. For computational emotion classification, multi-layer neural network using feature values extracted from heart rate and acceleration plethysmogram is used to discriminate emotional class. In experiments of computational emotion classification, an average of 38.3% classification rate is attained in three emotions - positive, negative, and neutral.