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Modeling emotional content of music using system identification

TitleModeling emotional content of music using system identification
Publication TypeJournal Article
Year of Publication2005
AuthorsKorhonen, M., D. A. Clausi, and E. Jernigan
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pagination588 - 599
Keywordsarousal dimension, Artificial Intelligence, Automated, average R/sup 2/ statistic, Computer Simulation, emotion recognition, Emotions, Humans, linear model structure, Models, music, music emotional content modeling, Pattern Recognition, Psychological, Psychometrics, system identification, time series, valence dimension

Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.