Article ID: | iaor20071069 |
Country: | United States |
Volume: | 18 |
Issue: | 3 |
Start Page Number: | 285 |
End Page Number: | 293 |
Publication Date: | Jun 2006 |
Journal: | INFORMS Journal On Computing |
Authors: | Conklin Darrell, Anagnostopoulou Christina |
Keywords: | music |
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.