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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)

Abstract

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.

Keywords

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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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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