Computers and musical style
Humdrum and Kern: selective feature encoding
Beyond MIDI
Representation and Discovery of Vertical Patterns in Music
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
String pattern matching for a deluge survival kit
Handbook of massive data sets
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
Melodic analysis with segment classes
Machine Learning
Computer Music Journal
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
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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.