Towards User-Adaptive Structuring and Organization of Music Collections

  • Sebastian Stober
  • Andreas Nürnberger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5811)


We present a prototype system for organization and exploration of music archives that adapts to the user’s way of structuring music collections. Initially, a growing self-organizing map is induced that clusters the music collection. The user has then the possibility to change the location of songs on the map by simple drag-and-drop actions. Each movement of a song causes a change in the underlying similarity measure based on a quadratic optimization scheme. As a result, the location of other songs is modified as well. Experiments simulating user interaction with the system show, that during this stepwise adaptation the similarity measure indeed converges to one that captures how the user compares songs. This utimately leads to an individually adapted presentation that is intuitively understandable to the user and thus eases access to the database.


Similarity Measure Weighting Scheme Music Information Retrieval Simulated User Music Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aucouturier, J.-J., Pachet, F.: Improving timbre similarity: How high is the sky? Journal of Negative Results in Speech and Audio Sciences 1(1) (2004)Google Scholar
  2. 2.
    Bärecke, T., Kijak, E., Nürnberger, A., Detyniecki, M.: Video navigation based on self-organizing maps. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, pp. 340–349. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Baumann, S., Halloran, J.: An ecological approach to multimodal subjective music similarity perception. In: Proc. of CIM 2004 (2004)Google Scholar
  4. 4.
    Mandel, M., Ellis, D.: Song-level features and support vector machines for music classification. In: Proc. of ISMIR 2005 (2005)Google Scholar
  5. 5.
    McEnnis, D., McKay, C., Fujinaga, I., Depalle, P.: jAudio: An feature extraction library. In: Proc. of ISMIR 2005 (2005)Google Scholar
  6. 6.
    Mörchen, F., Ultsch, A., Nöcker, M., Stamm, C.: Databionic visualization of music collections according to perceptual distance. In: Proc. of ISMIR 2005 (2005)Google Scholar
  7. 7.
    Neumayer, R., Dittenbach, M., Rauber, A.: PlaySOM and PocketSOMPlayer, alternative interfaces to large music collections. In: Proc. of ISMIR 2005 (2005)Google Scholar
  8. 8.
    Nürnberger, A., Detyniecki, M.: Weighted self-organizing maps - incorporating user feedback. In: Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Proc. of the joined 13th Int. Conf. (2003)Google Scholar
  9. 9.
    Nürnberger, A., Klose, A.: Improving clustering and visualization of multimedia data using interactive user feedback. In: Proc. of the 9th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2002 (2002)Google Scholar
  10. 10.
    Oliver, N., Kreger-Stickles, L.: PAPA: Psychology and purpose-aware automatic playlist generation. In: Proc. of ISMIR 2006 (2006)Google Scholar
  11. 11.
    Pampalk, E., Dixon, S., Widmer, G.: Exploring music collections by browsing different views. In: Proc. of ISMIR 2003 (2003)Google Scholar
  12. 12.
    Pampalk, E., Rauber, A., Merkl, D.: Content-based organization and visualization of music archives. In: Proc. of ACM MULTIMEDIA 2002 (2002)Google Scholar
  13. 13.
    Pauws, S., Eggen, B.: PATS: Realization and user evaluation of an automatic playlist generator. In: Proc. of ISMIR 2002 (2002)Google Scholar
  14. 14.
    Rauber, A., Pampalk, E., Merkl, D.: Using psycho-acoustic models and self-organizing maps to create a hierarchical structuring of music by musical styles. In: Proc. of ISMIR 2002 (2002)Google Scholar
  15. 15.
    Reinhard, J., Stober, S., Nürnberger, A.: Enhancing chord classification through neighbourhood histograms. In: Proc. of the 6th International Workshop on Content-Based Multimedia Indexing, CBMI 2008 (2008)Google Scholar
  16. 16.
    Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)CrossRefGoogle Scholar
  17. 17.
    Schedl, M.: The CoMIRVA Toolkit for Visualizing Music-Related Data. Technical report, Johannes Kepler University Linz (2006)Google Scholar
  18. 18.
    Stober, S., Nürnberger, A.: User modelling for interactive user-adaptive collection structuring. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds.) AMR 2007. LNCS, vol. 4918, pp. 95–108. Springer, Heidelberg (2008)Google Scholar
  19. 19.
    Tzanetakis, G.: Marsyas submission to MIREX 2007. In: Proc. of ISMIR 2007 (2007)Google Scholar
  20. 20.
    Tzanetakis, G., Jones, R., McNally, K.: Stereo panning features for classifying recording production style. In: Proc. of ISMIR 2007 (2007)Google Scholar
  21. 21.
    Vignoli, F., Pauws, S.: A music retrieval system based on user driven similarity and its evaluation. In: Proc. of ISMIR 2005 (2005)Google Scholar
  22. 22.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sebastian Stober
    • 1
  • Andreas Nürnberger
    • 1
  1. 1.Data and Knowledge Engineering Group, Faculty of Computer ScienceOtto-von-Guericke-University MagdeburgMagdeburgGermany

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