Classification of classic Turkish music makams by using deep belief networks

Merve Ayyuce Kizrak Sagun; Bulent Bolat

doi:10.1109/INISTA.2016.7571850

Classification of classic Turkish music makams by using deep belief networks

Merve Ayyuce Kizrak Sagun, Bulent Bolat

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

5 Citations (Scopus)

Abstract

Automatic classification of makams from sound data is a challenging yet rarely studied topic. In this work, it is aimed to develop an MIR system which determines a song's makam. To overcome this problem, mel frequency cepstral coefficients were utilized as features. Five classifiers were considered. The best result was obtained by deep belief network as 93.10 which is comparable to the recent works.

Original language	English
Title of host publication	Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016
Editors	Tulay Yuldirim, Mirel Cosulschi, Adina Magda Florea, Costin Badica, Petia Koprinkova-Hristova
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467399104
DOIs	https://doi.org/10.1109/INISTA.2016.7571850
Publication status	Published - 19 Sept 2016
Externally published	Yes
Event	2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016 - Sinaia, Romania Duration: 2 Aug 2016 → 5 Aug 2016

Publication series

Name	Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016

Conference

Conference	2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016
Country/Territory	Romania
City	Sinaia
Period	2/08/16 → 5/08/16

Keywords

Classic Turkish Music Makams
Deep Belief Networks
Generalized Regression Neural Network
Mel-Frequency Cepstrum Coefficients
Probabilistic Neural Network
Radial Basis Function Network
Support Vector Machine

Access to Document

10.1109/INISTA.2016.7571850

Cite this

Sagun, M. A. K., & Bolat, B. (2016). Classification of classic Turkish music makams by using deep belief networks. In T. Yuldirim, M. Cosulschi, A. M. Florea, C. Badica, & P. Koprinkova-Hristova (Eds.), Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016 Article 7571850 (Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INISTA.2016.7571850

Sagun, Merve Ayyuce Kizrak ; Bolat, Bulent. / Classification of classic Turkish music makams by using deep belief networks. Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016. editor / Tulay Yuldirim ; Mirel Cosulschi ; Adina Magda Florea ; Costin Badica ; Petia Koprinkova-Hristova. Institute of Electrical and Electronics Engineers Inc., 2016. (Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016).

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title = "Classification of classic Turkish music makams by using deep belief networks",

abstract = "Automatic classification of makams from sound data is a challenging yet rarely studied topic. In this work, it is aimed to develop an MIR system which determines a song's makam. To overcome this problem, mel frequency cepstral coefficients were utilized as features. Five classifiers were considered. The best result was obtained by deep belief network as 93.10 which is comparable to the recent works.",

keywords = "Classic Turkish Music Makams, Deep Belief Networks, Generalized Regression Neural Network, Mel-Frequency Cepstrum Coefficients, Probabilistic Neural Network, Radial Basis Function Network, Support Vector Machine",

author = "Sagun, {Merve Ayyuce Kizrak} and Bulent Bolat",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016 ; Conference date: 02-08-2016 Through 05-08-2016",

year = "2016",

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doi = "10.1109/INISTA.2016.7571850",

language = "English",

series = "Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Tulay Yuldirim and Mirel Cosulschi and Florea, {Adina Magda} and Costin Badica and Petia Koprinkova-Hristova",

booktitle = "Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016",

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Sagun, MAK & Bolat, B 2016, Classification of classic Turkish music makams by using deep belief networks. in T Yuldirim, M Cosulschi, AM Florea, C Badica & P Koprinkova-Hristova (eds), Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016., 7571850, Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016, Sinaia, Romania, 2/08/16. https://doi.org/10.1109/INISTA.2016.7571850

Classification of classic Turkish music makams by using deep belief networks. / Sagun, Merve Ayyuce Kizrak; Bolat, Bulent.
Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016. ed. / Tulay Yuldirim; Mirel Cosulschi; Adina Magda Florea; Costin Badica; Petia Koprinkova-Hristova. Institute of Electrical and Electronics Engineers Inc., 2016. 7571850 (Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016).

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

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AU - Bolat, Bulent

PY - 2016/9/19

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AB - Automatic classification of makams from sound data is a challenging yet rarely studied topic. In this work, it is aimed to develop an MIR system which determines a song's makam. To overcome this problem, mel frequency cepstral coefficients were utilized as features. Five classifiers were considered. The best result was obtained by deep belief network as 93.10 which is comparable to the recent works.

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KW - Deep Belief Networks

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KW - Radial Basis Function Network

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PB - Institute of Electrical and Electronics Engineers Inc.

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Sagun MAK, Bolat B. Classification of classic Turkish music makams by using deep belief networks. In Yuldirim T, Cosulschi M, Florea AM, Badica C, Koprinkova-Hristova P, editors, Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7571850. (Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016). doi: 10.1109/INISTA.2016.7571850

Classification of classic Turkish music makams by using deep belief networks

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