Abstract

In this article a number of musical features are extracted from a large musical database and these were subsequently used to build four composer-classification models. The first two models, an if–then rule set and a decision tree, result in an understanding of stylistic differences between Bach, Haydn, and Beethoven. The other two models, a logistic regression model and a support vector machine classifier, are more accurate. The probability of a piece being composed by a certain composer given by the logistic regression model is integrated into the objective function of a previously developed variable neighborhood search algorithm that can generate counterpoint. The result is a system that can generate an endless stream of contrapuntal music with composer-specific characteristics that sounds pleasing to the ear. This system is implemented as an Android app called FuX.

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Additional Information

ISSN
1531-5169
Print ISSN
0148-9267
Pages
pp. 71-91
Launched on MUSE
2015-09-18
Open Access
No
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