Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity

  • Markus Schedl
  • Peter Knees
  • Gerhard Widmer
Conference paper

DOI: 10.1007/11751069_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3902)
Cite this paper as:
Schedl M., Knees P., Widmer G. (2006) Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity. In: Kronland-Martinet R., Voinier T., Ystad S. (eds) Computer Music Modeling and Retrieval. CMMR 2005. Lecture Notes in Computer Science, vol 3902. Springer, Berlin, Heidelberg

Abstract

Discovering artists that can be considered as prototypes for particular genres or styles of music is a challenging and interesting task. Based on preliminary work, we elaborate an improved approach to rank artists according to their prototypicality. To calculate such a ranking, we use asymmetric similarity matrices obtained via co-occurrence analysis of artist names on web pages. In order to avoid distortions of the ranking due to ambiguous artist names, e.g. bands whose name equal common speech words (like “Kiss” or “Bush”), we introduce a penalization function. Our approach is demonstrated on a data set containing 224 artists from 14 genres.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Markus Schedl
    • 1
    • 2
  • Peter Knees
    • 1
  • Gerhard Widmer
    • 1
    • 2
  1. 1.Department of Computational PerceptionJohannes Kepler University (JKU)LinzAustria
  2. 2.Austrian Research Institute for Artificial Intelligence (FAI)ViennaAustria

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