Modeling emotional content of music using system identification.
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.
Korhonen
Mark D
MD
University of Waterloo, Waterloo, ON N2L 3G1 Canada. mdkorhon@alumni.uwaterloo.ca
Clausi
David A
DA
Jernigan
M Ed
ME
D001185:Artificial Intelligence; D003198:Computer Simulation; D004644:Emotions / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D009146:Music / Q000523:psychology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011594:Psychometrics / Q000379:methods*