@article{2979312,
    title = "Segmental pattern discovery in music",
    author = "Conklin, D. and Anagnostopoulou, C.",
    journal = "INFORMS Journal on Computing",
    year = "2006",
    volume = "18",
    number = "3",
    pages = "285-293",
    issn = "1091-9856, 1526-5528",
    doi = "10.1287/ijoc.1040.0122",
    abstract = "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. © 2006 INFORMS."
}