The NEXTPITCH Learning Classifier System: Representation, Information Theory and Performance

NEXTPITCH, a learning classifier system (LCS) using genetic algorithms, inductively learns to predict the next note in a musical melody. NEXTPITCH models human music learning by developing the rules that represent actual pitch transitions in the melody. In this article, the author addresses the issu...

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Bibliographic Details
Published inLeonardo (Oxford) Vol. 36; no. 1; pp. 47 - 50
Main Author Federman, Francine
Format Journal Article
LanguageEnglish
Published 238 Main St., Suite 500, Cambridge, MA 02142-1046, USA MIT Press 01.01.2003
The MIT Press
Subjects
Aural learning
Binary codes
Choirs
Information representations
Information theory
Machine learning
Melody
Music learning
Musical performance
Sound pitch
Special Section: Genetic Algorithms in Visual Art and Music
Online AccessGet full text
ISSN0024-094X
1530-9282
DOI10.1162/002409403321152301

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Summary:NEXTPITCH, a learning classifier system (LCS) using genetic algorithms, inductively learns to predict the next note in a musical melody. NEXTPITCH models human music learning by developing the rules that represent actual pitch transitions in the melody. In this article, the author addresses the issues of (1) the impact of the representation of a domain (the encoding of the characteristics of the field of study) on the performance of an LCS and (2) the classification of the input (the melodies to be learned) to an LCS in order to determine the highest percentage of correct next-note predictions.
Bibliography:February, 2003
ISSN:0024-094X
1530-9282
DOI:10.1162/002409403321152301