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Segmental Pattern Discovery in Music

Department of Computing, City University London, EC1V 0HB, United Kingdom
Faculty of Music, School of Philosophy, University of Athens, Panepistemiopolis, 15784 Athens, Greece
conklin{at}city.ac.uk
chrisa{at}music.uoa.gr

In this paper we describe a new method for discovering recurrent patterns in a corpus of segmented melodies. Elements of patterns in this scheme do not represent individual notes but rather represent melodic segments that are sequences of notes. A new knowledge representation for segmental patterns is designed, and a pattern discovery algorithm based on suffix trees is used to discover segmental patterns in large corpora. The method is applied to a large collection of melodies, including Nova Scotia folk songs, Bach chorale melodies, and sections from the Essen folk song database. Patterns are ranked using a statistical significance method that integrates pattern self-overlap, length, and frequency in a corpus into a single measure. A musical interpretation of some of the statistically significant discovered patterns is presented.

Key words: data mining; pattern discovery; music analysis; knowledge representation
History: received March 2004; revised August 2004; accepted November 2004.