From Improved Auto-Taggers to Improved Music Similarity Measures

نویسندگان

  • Klaus Seyerlehner
  • Markus Schedl
  • Reinhard Sonnleitner
  • David Hauger
  • Bogdan Ionescu
چکیده

This paper focuses on the relation between automatic tag prediction and music similarity. Intuitively music similarity measures based on auto-tags should profit from the improvement of the quality of the underlying audio tag predictors. We present classification experiments that verify this claim. Our results suggest a straight forward way to further improve content-based music similarity measures by improving the underlying auto-taggers.

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تاریخ انتشار 2012