Using Heuristic Optimization for Segmentation of Symbolic Music

  • Brigitte Rafael
  • Stefan Oertl
  • Michael Affenzeller
  • Stefan Wagner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5717)


Solving the segmentation problem for music is a key issue in music information retrieval (MIR). Structural information about a composition achieved by music segmentation can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. Various approaches using genetic algorithms have already been introduced to the field of media segmentation including image and video segmentation as segmentation problems usually have complex fitness landscapes. The authors of this paper present an approach to apply genetic algorithms to the music segmentation problem.


Genetic Algorithm Tournament Selection Heuristic Optimization Segmentation Problem Video Segmentation 
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.
    Abdulghafour, M.: Image segmentation using fuzzy logic and genetic algorithms. In: WSCG (2003)Google Scholar
  2. 2.
    Affenzeller, M., Wagner, S.: Offspring selection: A new self-adaptive selection scheme for genetic algorithms. In: Adaptive and Natural Computing Algorithms, pp. 218–221 (2005)Google Scholar
  3. 3.
    Chiu, P., Girgensohn, A., Wolf, P., Rieffel, E., Wilcox, L.: A genetic algorithm for video segmentation and summarization. In: IEEE International Conference on Multimedia and Expo, pp. 1329–1332 (2000)Google Scholar
  4. 4.
    Jehan, T.: Hierarchical multi-class self similarities. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 311–314 (2005)Google Scholar
  5. 5.
    Jensen, K.: Multiple scale music segmentation using rhythm, timbre, and harmony. EURASIP Journal on Applied Signal Processing 2007(1) (2007)Google Scholar
  6. 6.
    Lee, K., Cremer, M.: Segmentation-based lyrics-audio alignment using dynamic programming. In: Proceedings of the 9th International Conference on Music Information Retrieval, pp. 395–400 (2008)Google Scholar
  7. 7.
    Levy, M., Noland, K., Sandler, M.: A comparison of timbral and harmonic music segmentation algorithms. In: Proceedings of the Acoustics, Speech, and Signal Processing, vol. 4, pp. 1433–1436 (2007)Google Scholar
  8. 8.
    Maulik, U.: Medical image segmentation using genetic algorithms. IEEE Transactions on Information Technology in Biomedicine 13(2), 166–173 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)CrossRefzbMATHGoogle Scholar
  10. 10.
    Mueller, M., Ewert, S.: Joint structure analysis with applications to music annotation and synchronization. In: Proceedings of the 9th International Conference on Music Information Retrieval, pp. 389–394 (2008)Google Scholar
  11. 11.
    Paulus, J., Klapuri, A.: Music structure analysis by finding repeated parts. In: AMCMM 2006: Proceedings of the 1st ACM workshop on Audio and music computing multimedia, p. 5968. ACM Press, New York (2006)Google Scholar
  12. 12.
    Peiszer, E.: Automatic audio segmentation: Segment boundary and structure detection in popular music. Master’s thesis, Vienna University of Technology, Vienna, Austria (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Brigitte Rafael
    • 1
  • Stefan Oertl
    • 1
  • Michael Affenzeller
    • 2
  • Stefan Wagner
    • 2
  1. 1.Re-Compose GmbHViennaAustria
  2. 2.School of Informatics, Communications and Media Heuristic and Evolutionary Algorithms LaboratoryUpper Austrian University of Applied SciencesHagenbergAustria

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