Title
Composer classification models for music-theory buildingComposer classification models for music-theory building
Author
Faculty/Department
Faculty of Applied Economics
Research group
Engineering Management
Publication type
bookPart
Publication
Springer International Publishing, [*]
Subject
Computer. Automation
Source (book)
Computational music analysis / Meredith, David [edit.]
ISBN - Hoofdstuk
978-3-319-25929-1
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The task of recognizing a composer by listening to a musical piece used to be reserved for experts in music theory. The problems we address here are, first, that of constructing an automatic system that is able to distinguish between music written by different composers; and, second, identifying the musical properties that are important for this task. We take a data-driven approach by scanning a large database of existing music and develop five types of classification model that can accurately discriminate between three composers (Bach, Haydn and Beethoven). More comprehensible models, such as decision trees and rulesets, are built, as well as black-box models such as support vector machines. Models of the first type offer important insights into the differences between composer styles, while those of the second type provide a performance benchmark.
Handle