Music Genre Classi cation Using Gaussian Process Models and Gibbs samplers
Keywords
Loading...
Authors
Issue Date
2021-04-21
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
Music genres are groups with certain musical characteristics. They can be
used to create structure within the large amount of music that exists, for
example via genre classi cation. Genre classi cation has been automated
using Machine Learning. In previous research, Gaussian Processes have
successfully been used to classify genres with a reasonable accuracy (60,9%
over 6 genres), using relatively few samples (20 per genre).
In this paper, several genre classi cation approaches are compared: a
Gaussian Process, a Gibbs sampler and as a baseline a Support Vector Ma-
chine. First, the optimal kernel for both the Gaussian Process and Support
Vector Machine is determined. Then, the models are compared. The models
are trained with a range of di erent sample sizes and accuracy is determined
using the same testing set. The accuracies that the models achieve are then
compared.
Using the Support Vector Machine with the polynomial kernel yielded
the highest accuracies with the polynomial kernel and the Gaussian Process
achieved the highest accuracies with the Mat ern kernel. When trained on the
same data, the Gaussian Process outperforms the Support Vector Machine.
The Gaussian Process reaches an accuracy of 73,1%, while the Support
Vector Machine reaches an accuracy of 61,7%. The Gibbs sampler proves
to perform relatively poor on this classi cation problem, as it yields an
accuracy of 48,2%.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen