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. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Rafael, B., Oertl, S., Affenzeller, M., & Wagner, S. (2009). Using heuristic optimization for segmentation of symbolic music. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5717 LNCS, pp. 641–648). https://doi.org/10.1007/978-3-642-04772-5_83
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