Mining Scalar Representations in a Non-tagged Music Database

  • Rory A. Lewis
  • Wenxin Jiang
  • Zbigniew W. Raś
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4994)

Abstract

In the continuing investigation of the relationship between music and emotions it is recognized that MPEG-7 based MIR systems are the state-of-the-art. Also, it is known that non-temporal systems are diametrically unconducive to pitch analysis, an imperative for key and scalar analysis which determine emotions in music. Furthermore, even in a temporal MIR system one can only find the key if the scale is known or vice-versa, one can only find the scale if the key is known. We introduce a new MIRAI-based decision-support system that, given a blind database of music files, can successfully search for both the scale and the key of an unknown song in a music database and accordingly link each song to its set of scales and possible emotional states.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chew, E.: Music information processing: a new application for operations researchers. Bulletin of AIROnews 7(3), 9–14 (2002)Google Scholar
  2. 2.
    Hevner, K.: Experimental studies of the elements of expression in music. American Journal of Psychology 48, 246–268 (1936)CrossRefGoogle Scholar
  3. 3.
    Lewis, R., Zhang, X., Raś, Z.: Knowledge discovery based identification of musical pitches and instruments in polyphonic sounds. International Journal of Engineering Applications of Artificial Intelligence 20(5), 637–645 (2007)CrossRefGoogle Scholar
  4. 4.
    Lewis, R., Raś, Z.: Rules for processing and manipulating scalar music theory. In: Proceedings of MUE 2007, IEEE Conference, Seoul, Korea, pp. 26–28 (2007)Google Scholar
  5. 5.
    Lewis, R., Wieczorkowska, A.: A Categorization of musical instrument sounds based on numerical parameters. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 784–792. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Li, T., Ogihara, M.: Detecting emotion in music, in ISMIR 2003 Proceed (2003), http://ismir2003.ismir.net/papers/Li.PDF
  7. 7.
    McClellan, R.: The healing forces of music. In: Element Inc., Rockport, MA (1966)Google Scholar
  8. 8.
    Pawlak, Z.: Information systems - theoretical foundations. Information Systems Journal 6, 205–218 (1991)Google Scholar
  9. 9.
    Raś, Z., Zhang, X., Lewis, R.: MIRAI: Multi-hierarchical, FS-tree based music information retrieval system. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 28–30. Springer, Heidelberg (2007)Google Scholar
  10. 10.
    Sevgen, A.: The science of musical sound. Scientific American Books Inc., New York (1983)Google Scholar
  11. 11.
    Sloboda, J.A., ONeill, S.A.: Emotions in everyday listening to music. In: Juslin, P.N., Sloboda, J.A. (eds.) Music and Emotion: Theory and Research, pp. 415–430. Oxford Univ. Press, Oxford (2001)Google Scholar
  12. 12.
    Valentinuzzi, M.E., Arias, N.E.: Human psychophysiological perception of musical scales and nontraditional music. IEEE/Eng Medicine and Biol. Mag. 18(2), 54–60 (1999)CrossRefGoogle Scholar
  13. 13.
    Vink, A.: Music and Emotion, living apart together: a relationship between music psychology and music therapy. Nordic Journal of Music Therapy 10(2), 144–158 (2001)CrossRefGoogle Scholar
  14. 14.
    Wieczorkowska, A., Synak, P., Lewis, R., Raś, Z.: Creating reliable database for experiments on extracting emotions from music. In: IIPWM 2005 Proceedings. Advances in Soft Computing, pp. 395–402. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rory A. Lewis
    • 1
  • Wenxin Jiang
    • 1
  • Zbigniew W. Raś
    • 1
  1. 1.Dept. of Comp. ScienceUniversity of North CarolinaCharlotteUSA

Personalised recommendations