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From Improved Auto-Taggers to Improved Music Similarity Measures

  • Klaus Seyerlehner
  • Markus Schedl
  • Reinhard Sonnleitner
  • David Hauger
  • Bogdan Ionescu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8382)

Abstract

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.

Keywords

Music information retrieval Music similarity Auto-tagging Music recommendation Tag prediction 

Notes

Acknowledgements

This research is supported by the Austrian Science Funds (FWF): P22856-N23 and Z159.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Klaus Seyerlehner
    • 1
  • Markus Schedl
    • 1
  • Reinhard Sonnleitner
    • 1
  • David Hauger
    • 1
  • Bogdan Ionescu
    • 2
  1. 1.Department of Computational PerceptionJohannes Kepler UniversityLinzAustria
  2. 2.Image Processing and Analysis LaboratoryUniversity Politehnica of BucharestBucharestRomania

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