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Issue title: Machine Learning and Music
Guest editors: Darrell Conklinx, Christina Anagnostopoulouy and Rafael Ramirezz
Article type: Research Article
Authors: Conklin, Darrella; b
Affiliations: [a] Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastián, Spain | [b] IKERBASQUE, Basque Foundation for Science, Bilbao, Spain. E-mail: conklin@ikerbasque.org | [x] City University London, London, UK | [y] University of Athens, Athens, Greece | [z] Universitat Pompeu Fabra, Baralona, Spain
Abstract: This paper proposes a new view of pattern discovery in music: inductive querying a corpus for maximally general distinctive patterns. A pattern is distinctive if it is over-represented with respect to an anticorpus, and maximally general distinctive if no subsuming pattern is also distinctive. An algorithm for maximally general distinctive pattern discovery is presented and applied to folk song melodies from three geographic regions, and to chord sequences from three music genres. Distinctive patterns are applicable to a wide range of music analysis tasks where an anticorpus can be defined and contrasted with an analysis corpus.
Keywords: Pattern discovery, distinctive pattern, subsumption, anticorpus, folk songs, chord sequences
DOI: 10.3233/IDA-2010-0438
Journal: Intelligent Data Analysis, vol. 14, no. 5, pp. 547-554, 2010
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