• DocumentCode
    110762
  • Title

    Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

  • Author

    Roda, Antonio ; Canazza, Sergio ; De Poli, Giovanni

  • Author_Institution
    Dept. of Inf. Eng., Univ. of Padova, Padua, Italy
  • Volume
    5
  • Issue
    4
  • fYear
    2014
  • fDate
    Oct.-Dec. 1 2014
  • Firstpage
    364
  • Lastpage
    376
  • Abstract
    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.
  • Keywords
    behavioural sciences; music; affective dimensions; affective potency; affective qualities clustering; arousal dimensions; classical music; complex audio excerpts; computable musical features; energy; features identification; loudness; modality; potency dimension; real music recordings; roughness; secondary emotion dimensions; spectral flux; tempo; tension; valence dimensions; valence-arousal plane; Emotion recognition; Music; Music information retrieval; Physiology; Stress; User interfaces; Music; affective dimensions; automated mood analysis; emotions; musical features; potency;
  • fLanguage
    English
  • Journal_Title
    Affective Computing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3045
  • Type

    jour

  • DOI
    10.1109/TAFFC.2014.2343222
  • Filename
    6866179