|Title||Modeling emotional content of music using system identification|
|Publication Type||Journal Article|
|Year of Publication||2005|
|Authors||Korhonen, M., D. A. Clausi, and E. Jernigan|
|Journal||IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics|
|Pagination||588 - 599|
|Keywords||arousal 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.