Classification Accuracy Is Not Enough: On the Evaluation of Music Genre Recognition Systems

Bob L. Sturm

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

96 Citationer (Scopus)
1691 Downloads (Pure)

Abstract

A recent review of the research literature evaluating music genre recognition (MGR) systems over the past two decades shows that most works (81\%) measure the capacity of a system to recognize genre by its classification accuracy. We show here, by implementing and testing three categorically different state-of-the-art MGR systems, that classification accuracy does not necessarily reflect the capacity of a system to recognize genre in musical signals. We argue that a more comprehensive analysis of behavior at the level of the music is needed to address the problem of MGR, and that measuring classification accuracy obscures the aim of MGR: to select labels indistinguishable from those a person would choose.
OriginalsprogEngelsk
TidsskriftJournal of Intelligent Information Systems
Vol/bind41
Udgave nummer3
Sider (fra-til)371-406
Antal sider36
ISSN0925-9902
DOI
StatusUdgivet - 2013

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