Automatic music classification into genres
Date Issued
2012-09
Author(s)
Madjarov, Gjorgji
Pesanski, Goran
Spasovski, Daniel
Gjorgjevikj, Dejan
Abstract
Musical genres are categorical labels created by humans to
characterize pieces of music. Although music genres are inexact and can
often be quite arbitrary and controversial, it is believed that certain
song characteristics like instrumentation, rhythmic structure, and harmonic content of the music are related to the genre. In this paper, the
task of automatic music genre classification is explored. Multiple features based on timbral texture, rhythmic content and pitch content are
extracted from a single music piece and used to train different classifiers
for genre prediction. The experiments were performed using features extracted from one or two 30 second segments from each song. For the
classification, two different architectures flat and hierarchical classification and three different classifiers (kNN, MLP and SVM) were tried.
The experiments were performed on the full feature set (316 features)
and on a PCA reduced feature set. The testing speed of the classifiers was
also measured.The experiments carried out on a large dataset containing more than 1700 music samples from ten different music genres have
shown accuracy of 69.1% for the flat classification architecture (utilizing
one against all SVM based classifiers). The accuracy obtained using the
hierarchical classification architecture was slightly lower 68.8%, but four
times faster than the flat architecture.
characterize pieces of music. Although music genres are inexact and can
often be quite arbitrary and controversial, it is believed that certain
song characteristics like instrumentation, rhythmic structure, and harmonic content of the music are related to the genre. In this paper, the
task of automatic music genre classification is explored. Multiple features based on timbral texture, rhythmic content and pitch content are
extracted from a single music piece and used to train different classifiers
for genre prediction. The experiments were performed using features extracted from one or two 30 second segments from each song. For the
classification, two different architectures flat and hierarchical classification and three different classifiers (kNN, MLP and SVM) were tried.
The experiments were performed on the full feature set (316 features)
and on a PCA reduced feature set. The testing speed of the classifiers was
also measured.The experiments carried out on a large dataset containing more than 1700 music samples from ten different music genres have
shown accuracy of 69.1% for the flat classification architecture (utilizing
one against all SVM based classifiers). The accuracy obtained using the
hierarchical classification architecture was slightly lower 68.8%, but four
times faster than the flat architecture.
Subjects
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