Müzik türlerinin i̇şitsel nitelikler, farkli smiflandiricilar ve nitelik seçme yöntemleri ile smiflandirilmasi

Yusuf Yaslan; Zehra Çataltepe

doi:10.1109/SIU.2006.1659762

Müzik türlerinin i̇şitsel nitelikler, farkli smiflandiricilar ve nitelik seçme yöntemleri ile smiflandirilmasi

Translated title of the contribution: Music genre classification using audio features, different classifiers and feature selection methods

Yusuf Yaslan^*, Zehra Çataltepe

^*Corresponding author for this work

Department of Computer Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

In this paper, performance of different classifiers (Fisher, linear, quadratik, Naive Bayes, Parzen, k-nearest neighbor) to determine the genre of a given music piece, using different audio feature sets, is examined. For each classifier, performances of feature sets obtained by feature selection and dimensionality reduction methods are also evaluated. Finally, classification accuracy is improved by combining different classifiers. A 10 genre data set of 1000 pieces is used in the experiments. Using a set of different classifiers, a test genre classification accuracy of around 79.6 ± 4.2 is obtained. This performance is better than 71.1 ± 7.3% which is the best that has been reported on this data set. Also, by combining different classifiers 80% classification accuracy is obtained.

Translated title of the contribution	Music genre classification using audio features, different classifiers and feature selection methods
Original language	Turkish
Title of host publication	2006 IEEE 14th Signal Processing and Communications Applications Conference
DOIs	https://doi.org/10.1109/SIU.2006.1659762
Publication status	Published - 2006
Event	2006 IEEE 14th Signal Processing and Communications Applications - Antalya, Turkey Duration: 17 Apr 2006 → 19 Apr 2006

Publication series

Name	2006 IEEE 14th Signal Processing and Communications Applications Conference
Volume	2006

Conference

Conference	2006 IEEE 14th Signal Processing and Communications Applications
Country/Territory	Turkey
City	Antalya
Period	17/04/06 → 19/04/06

Access to Document

10.1109/SIU.2006.1659762

Cite this

Yaslan, Y., & Çataltepe, Z. (2006). Müzik türlerinin i̇şitsel nitelikler, farkli smiflandiricilar ve nitelik seçme yöntemleri ile smiflandirilmasi. In 2006 IEEE 14th Signal Processing and Communications Applications Conference Article 1659762 (2006 IEEE 14th Signal Processing and Communications Applications Conference; Vol. 2006). https://doi.org/10.1109/SIU.2006.1659762

@inproceedings{73f21d60a86e4b3b866bc553417c0823,

title = "M{\"u}zik t{\"u}rlerinin {\.i}{\c s}itsel nitelikler, farkli smiflandiricilar ve nitelik se{\c c}me y{\"o}ntemleri ile smiflandirilmasi",

abstract = "In this paper, performance of different classifiers (Fisher, linear, quadratik, Naive Bayes, Parzen, k-nearest neighbor) to determine the genre of a given music piece, using different audio feature sets, is examined. For each classifier, performances of feature sets obtained by feature selection and dimensionality reduction methods are also evaluated. Finally, classification accuracy is improved by combining different classifiers. A 10 genre data set of 1000 pieces is used in the experiments. Using a set of different classifiers, a test genre classification accuracy of around 79.6 ± 4.2 is obtained. This performance is better than 71.1 ± 7.3% which is the best that has been reported on this data set. Also, by combining different classifiers 80% classification accuracy is obtained.",

author = "Yusuf Yaslan and Zehra {\c C}ataltepe",

year = "2006",

doi = "10.1109/SIU.2006.1659762",

language = "T{\"u}rk{\c c}e",

isbn = "1424402395",

series = "2006 IEEE 14th Signal Processing and Communications Applications Conference",

booktitle = "2006 IEEE 14th Signal Processing and Communications Applications Conference",

note = "2006 IEEE 14th Signal Processing and Communications Applications ; Conference date: 17-04-2006 Through 19-04-2006",

}

Yaslan, Y & Çataltepe, Z 2006, Müzik türlerinin i̇şitsel nitelikler, farkli smiflandiricilar ve nitelik seçme yöntemleri ile smiflandirilmasi. in 2006 IEEE 14th Signal Processing and Communications Applications Conference., 1659762, 2006 IEEE 14th Signal Processing and Communications Applications Conference, vol. 2006, 2006 IEEE 14th Signal Processing and Communications Applications, Antalya, Turkey, 17/04/06. https://doi.org/10.1109/SIU.2006.1659762

Müzik türlerinin i̇şitsel nitelikler, farkli smiflandiricilar ve nitelik seçme yöntemleri ile smiflandirilmasi. / Yaslan, Yusuf; Çataltepe, Zehra.
2006 IEEE 14th Signal Processing and Communications Applications Conference. 2006. 1659762 (2006 IEEE 14th Signal Processing and Communications Applications Conference; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Müzik türlerinin i̇şitsel nitelikler, farkli smiflandiricilar ve nitelik seçme yöntemleri ile smiflandirilmasi

AU - Yaslan, Yusuf

AU - Çataltepe, Zehra

PY - 2006

Y1 - 2006

N2 - In this paper, performance of different classifiers (Fisher, linear, quadratik, Naive Bayes, Parzen, k-nearest neighbor) to determine the genre of a given music piece, using different audio feature sets, is examined. For each classifier, performances of feature sets obtained by feature selection and dimensionality reduction methods are also evaluated. Finally, classification accuracy is improved by combining different classifiers. A 10 genre data set of 1000 pieces is used in the experiments. Using a set of different classifiers, a test genre classification accuracy of around 79.6 ± 4.2 is obtained. This performance is better than 71.1 ± 7.3% which is the best that has been reported on this data set. Also, by combining different classifiers 80% classification accuracy is obtained.

AB - In this paper, performance of different classifiers (Fisher, linear, quadratik, Naive Bayes, Parzen, k-nearest neighbor) to determine the genre of a given music piece, using different audio feature sets, is examined. For each classifier, performances of feature sets obtained by feature selection and dimensionality reduction methods are also evaluated. Finally, classification accuracy is improved by combining different classifiers. A 10 genre data set of 1000 pieces is used in the experiments. Using a set of different classifiers, a test genre classification accuracy of around 79.6 ± 4.2 is obtained. This performance is better than 71.1 ± 7.3% which is the best that has been reported on this data set. Also, by combining different classifiers 80% classification accuracy is obtained.

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U2 - 10.1109/SIU.2006.1659762

DO - 10.1109/SIU.2006.1659762

M3 - Konferans katkısı

AN - SCOPUS:34247151561

SN - 1424402395

SN - 9781424402397

T3 - 2006 IEEE 14th Signal Processing and Communications Applications Conference

BT - 2006 IEEE 14th Signal Processing and Communications Applications Conference

T2 - 2006 IEEE 14th Signal Processing and Communications Applications

Y2 - 17 April 2006 through 19 April 2006

ER -

Müzik türlerinin i̇şitsel nitelikler, farkli smiflandiricilar ve nitelik seçme yöntemleri ile smiflandirilmasi

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