@article {416,
	title = {Modeling emotional content of music using system identification},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics},
	volume = {36},
	year = {2005},
	pages = {588 - 599},
	abstract = {<p>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.</p>
},
	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},
	issn = {1083-4419},
	doi = {http://dx.doi.org/10.1109/TSMCB.2005.862491},
	author = {M Korhonen and D A. Clausi and E Jernigan}
}
