Using Machine-Learning Methods for Musical Style Modeling

Authors:
Shlomo Dubnov;Gerard Assayag;Olivier Lartillot;Gill Bejerano
Affiliations:
Ben-Gurion University;Institut de Recherche et Coordination Acoustique/Musique;Institut de Recherche et Coordination Acoustique/Musique;Hebrew University
Venue:
Computer
Year:
2003

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Abstract

Constructing a musical theory from examples presents an intellectual challenge that could foster a range of new creative applications. Thus, the authors sought to apply machine-learning methods to the problem of musical style modeling. Their work has produced examples of musical generation and applications to a computer-aided composition system. Using statistical and information-theoretic tools that analyze musical pieces, they seek to capture some of the regularity apparent in the composition process. The resulting models can be used for inference and prediction, and to generate new works that imitate the great masters' styles.