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

Yusuf Yaslan^*
, Zehra Çataltepe

^*Bu çalışma için yazışmadan sorumlu yazar

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

4 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Music genre classification using audio features, different classifiers and feature selection methods
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2006 IEEE 14th Signal Processing and Communications Applications Conference
DOI'lar	https://doi.org/10.1109/SIU.2006.1659762
Yayın durumu	Yayınlandı - 2006
Etkinlik	2006 IEEE 14th Signal Processing and Communications Applications - Antalya, Türkiye Süre: 17 Nis 2006 → 19 Nis 2006

Yayın serisi

Adı	2006 IEEE 14th Signal Processing and Communications Applications Conference
Hacim	2006

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2006 IEEE 14th Signal Processing and Communications Applications
Ülke/Bölge	Türkiye
Şehir	Antalya
Periyot	17/04/06 → 19/04/06

Belgeye Erişim

10.1109/SIU.2006.1659762

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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 Makale 1659762 (2006 IEEE 14th Signal Processing and Communications Applications Conference; Hac. 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, hac. 2006, 2006 IEEE 14th Signal Processing and Communications Applications, Antalya, Türkiye, 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; Hac. 2006).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

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.

UR - https://www.scopus.com/pages/publications/34247151561

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Belgeye Erişim

Diğer Dosyalar ve Linkler

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