Accessing Music Digital Libraries by Combining Semantic Tags and Audio Content

  • Riccardo Miotto
  • Nicola Orio
Part of the Communications in Computer and Information Science book series (CCIS, volume 249)

Abstract

An interesting problem in accessing music digital libraries is how to combine the information of different sources in order to improve the retrieval effectiveness. This paper introduces an approach to represent a collection of tagged songs through an hidden Markov model with the purpose to develop a system that merges in the same framework both acoustic similarity and semantic descriptions. The former provides content-based information on song similarity, the latter provides context-aware information about individual songs. Experimental results show how the proposed model leads to better performances than approaches that rank songs using both a single information source and a their linear combination.

Keywords

Hide Markov Model Mean Average Precision Ranking List Semantic Description Semantic Label 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barrington, L., Oda, R., Lanckriet, G.: Smarter than genius? Human evaluation of music recommender systems. In: Proceedings of the International Conference on Music Information Retrieval, pp. 357–362 (2009)Google Scholar
  2. 2.
    Barrington, L., Lanckriet, G., Turnbull, D., Yazdani, M.: Combining audio content and social context for semantic music discovery. In: Proceedings of ACM SIGIR, pp. 387–394 (2009)Google Scholar
  3. 3.
    McFee, B., Lanckriet, G.: Heterogenous embedding for subjective artist similarity. In: Proceedings of the International Conference on Music Information Retrieval, pp. 513–518 (2009)Google Scholar
  4. 4.
    Slaney, M., Weinberger, K., White, W.: Learning a metric for music similarity. In: Proceedings of the International Conference on Music Information Retrieval, pp. 313–318 (2008)Google Scholar
  5. 5.
    Mandel, M., Ellis, D.P.W.: Song-level features and support vector machines for music classification. In: Proceedings of the International Conference on Music Information Retrieval, pp. 594–599 (2005)Google Scholar
  6. 6.
    Hoffman, M., Blei, D., Cook, P.: Content-based musical similarity computation using the hierarchical dirichlet process. In: Proceedings of the International Conference on Music Information Retrieval, pp. 349–354 (2008)Google Scholar
  7. 7.
    Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Semantic annotation and retrieval of music and sound effects. IEEE Transactions on Audio, Speech, and Language Processing 16, 467–476 (2008)CrossRefGoogle Scholar
  8. 8.
    Ness, S.R., Theocharis, A., Tzanetakis, G., Martins, L.G.: Improving automatic music tag annotation using stacked generalization of probabilistic svm outputs. In: Proceedings of ACM MULTIMEDIA, pp. 705–708 (2009)Google Scholar
  9. 9.
    Rabiner, L.: A tutorial on hidden Markov models and selected application. Proc. of the IEEE 77, 257–286 (1989)CrossRefGoogle Scholar
  10. 10.
    Shifrin, J., Pardo, B., Meek, C., Birmingham, W.: HMM-based musical query retrieval. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries, pp. 295–300 (2002)Google Scholar
  11. 11.
    Miotto, R., Orio, N.: Automatic identification of music works through audio matching. In: Proceedings of the European Conference on Digital Libraries, pp. 124–135 (2007)Google Scholar
  12. 12.
    Montecchio, N., Orio, N.: A discrete filter bank approach to audio to score matching for polyphonic music. In: Proceedings of the International Conference on Music Information Retrieval, pp. 495–500 (2009)Google Scholar
  13. 13.
    Raphael, C.: Automatic segmentation of acoustic musical signals using hidden markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 360–370 (1999)CrossRefGoogle Scholar
  14. 14.
    Khadkevich, M., Omologo, M.: Use of hidden markov models and factored language models for automatic chord recognition. In: Proceedings of the International Conference on Music Information Retrieval, pp. 561–566 (2009)Google Scholar
  15. 15.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)MATHGoogle Scholar
  16. 16.
    Kullback, S., Leibler, R.: On information and sufficiency. Annals of Mathematical Statistics 12, 79–86 (1951)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Towards musical query-by-semantic description using the CAL500 data set. In: Proceedings of ACM SIGIR, pp. 439–446 (2007)Google Scholar
  18. 18.
    Manning, C., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
  19. 19.
    Turnbull, D., Barrington, L., Lanckriet, G.: Five approaches to collecting tags for music. In: Proceedings of the International Conference on Music Information Retrieval, pp. 225–230 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Riccardo Miotto
    • 1
  • Nicola Orio
    • 1
  1. 1.Department of Information EngineeringUniversity of PaduaPaduaItaly

Personalised recommendations