From Improved Auto-Taggers to Improved Music Similarity Measures

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


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.


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



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


  1. 1.
    Berenzweig, A., Ellis, P., Lawrence, S.: Anchor space for classification and similarity measurement of music. In: Proceedings of the 2003 International Conference on Multimedia and Expo (ICME-03) (2003)Google Scholar
  2. 2.
    Bertin-Mahieux, T., Eck, D., Maillet, F., Lamere, P.: Autotagger: a model for predicting social tags from acoustic features on large music databases. J. New Music Res. 37(2), 115–135 (2008)CrossRefGoogle Scholar
  3. 3.
    Bogdanov, D., Serrã, J., Wack, N., Herrera, P., Serra, X.: Unifying low-level and high-level music similarity measures. IEEE Trans. Multimedia (2010)Google Scholar
  4. 4.
    Eck, D., Lamere, P., Mahieux, T.B., Green, S.: Automatic generation of social tags for music recommendation. In: Proceedings of the 21st Conference on Neural Infromation Processing Systems (NIPS-07) (2007)Google Scholar
  5. 5.
    Ionescu, B., Seyerlehner, K., Rasche, C., Vertan, C., Lambert, P.: Content-based video description for automatic video genre categorization. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.-W., Andreopoulos, Y., Breiteneder, C. (eds.) MMM 2012. LNCS, vol. 7131, pp. 51–62. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Law, E., Ahn, L.: Input-agreement: a new mechanism for collecting data using human computation games. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI-09) (2009)Google Scholar
  7. 7.
    Mandel, M.I., Ellis, D.P.W.: A web-based game for collecting music metadata. J. New Music Res. 37(2), 151–165 (2008)CrossRefGoogle Scholar
  8. 8.
    Ness, S., Theocharis, A., Tzanetakis, G., Martins, L.: Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs. In: Proceedings of the 17th ACM International Conference on Multimedia (2009)Google Scholar
  9. 9.
    Seyerlehner, K., Schedl, M., Knees, P., Sonnleitner, R.: A refined block-level feature set for classification, similarity and tag prediction. In: Online Proceedings of the 7th MIR Evaluation eXchange (MIREX-11) (2011)Google Scholar
  10. 10.
    Seyerlehner, K., Schedl, M., Pohle, T., Knees, P.: Using block-level features for genre classification, tag classification and music similarity estimation. In: Online Proceedings of the 6th MIR Evaluation eXchange (MIREX-10) (2010)Google Scholar
  11. 11.
    Seyerlehner, K., Widmer, G., Schedl, M., Knees, P.: Automatic music tag classification based on block-level features. In: Proceedings of the 7th Sound and Music Computing Conference (SMC-10) (2010)Google Scholar
  12. 12.
    Slaney, M.: Mixture of probability experts for audio retrieval and indexing. In: Proceedings of the IEEE International Conference on Multimedia and Expo (2002)Google Scholar
  13. 13.
    Turnbull, D., Barrington, L., Lanckriet, G.: Five approaches to collecting tags for music. In: Proceedings of the 9th International Conference on Music Information Retrieval (2008)Google Scholar
  14. 14.
    Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Semantic annotation and retrieval of music and sound effects. IEEE Trans. Audio Speech Lang. Process. 16(2), 467–476 (2008)CrossRefGoogle Scholar
  15. 15.
    West, K., Cox, S.: Incorporating cultural representations of features into audio music similarity estimation. IEEE Trans. Audio Speech Lang. Process. 18(3), 625–637 (2010)CrossRefGoogle Scholar
  16. 16.
    West, K., Cox, S., Lamere, P.: Incorporating machine-learning into music similarity estimation. In: Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia (AMCMM-06) (2006)Google Scholar
  17. 17.
    Whitman, B., Rifkin, R.: Musical query-by-description as a multiclass learning problem. In: IEEE Multimedia Signal Processing Conference (MMSP) (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Klaus Seyerlehner
    • 1
  • Markus Schedl
    • 1
    Email author
  • 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

Personalised recommendations