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Issue No. 04 - Oct.-Dec. (2014 vol. 5)
ISSN: 1949-3045
pp: 364-376
Antonio Roda , Department of Information Engineering, University of Padova, Italy
Sergio Canazza , Department of Information Engineering, University of Padova, Italy
Giovanni De Poli , Department of Information Engineering, University of Padova, Italy
The important role of the valence and arousal dimensions in representing and recognizing affective qualities in music is well established. There is less evidence for the contribution of secondary dimensions such as potency, tension and energy. In particular, previous studies failed to find significant relations between computable musical features and affective dimensions other than valence and arousal. Here we present two experiments aiming at assessing how musical features, directly computable from complex audio excerpts, are related to secondary emotion dimensions. To this aim, we imposed some constraints on the musical features, namely modality and tempo, of the stimuli.The results show that although arousal and valence dominate for many musical features, it is possible to identify features, in particular Roughness, Loudness, and SpectralFlux, that are significantly related to the potency dimension. As far as we know, this is the first study that gained more insight into the affective potency in the music domain by using real music recordings and a computational approach.
Music, Correlation, Stress, Indexes, Physiology, Music information retrieval, User interfaces

A. Roda, S. Canazza and G. De Poli, "Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane," in IEEE Transactions on Affective Computing, vol. 5, no. 4, pp. 364-376, 2014.
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