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Content-Based Music Discovery

  • Dirk Schönfuß
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6684)

Abstract

Music recommendation systems have become a valuable aid for managing large music collections and discovering new music. Our content-based recommendation system employs signal-based features and semantic music attributes generated using machine-based learning algorithms. In addition to playlist generation and music recommendation, we are exploring new usability concepts made possible by the analysis results. Functionality such as the mufin vision sound universe enables the user to discover his own music collection or even unknown catalogues in a new, more intuitive way.

Keywords

music visualization recommendation cloud clustering semantic attributes auto-tagging 

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References

  1. 1.
    Bahanovich, D., Collopy, D.: Music Experience and Behaviour in Young People. University of Hertfordshire, UK (2009)Google Scholar
  2. 2.
    Celma, O.: Music Recommendation and Discovery in the Long Tail. PhD-Thesis, Universitat Pompeu Fabra, Spain (2008)Google Scholar
  3. 3.
    Nielsen Soundscan: State of the industrie (2007), http://www.narm.com/2008Conv/StateoftheIndustry.pdf (July 22, 2009)

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dirk Schönfuß
    • 1
  1. 1.mufin GmbHDresdenGermany

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