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Music Genre Identification Using SVM and MFCC Feature Extraction
https://doi.org/10.22146/ijeis.70898 Septian Yogi Yehezkiel(1*), Yohanes Suyanto(2)
(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(*) Corresponding Author
Abstract
Indonesia is a very diverse country because it has a vast territory and is occupied by millions of people from various tribe. Therefore, traditional music in Indonesia is also diverse because each region has its own culture and art. In this study, the author used the Support Vector Machine(SVM) pattern recognition to identify the Indonesian traditional music genre. This genre identification system is able to produce an accuracy of 83% using MFCC.
Keywords : traditional music identification, Mel Frequency Cepstral Coefficient, Support Vector Machine.
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Rinaldi, A., Hendra dan Alamsyah, D., “Pengenalan Gender Melalui Suara dengan Algoritme Support Vector Machine”,vol.2, no.1. pp.47-54,2016.
Gupta, M., Bharti, S.S. dan Agarwa, S., “Support Vector Machine Based Jenis Kelamin Identification Using Voiced Speech Frames”,Fourth International Conference on Parallel, vol.3, no.2,pp. 16-20, 2016.
Setiawan, A. Hidayatno, and R. R. Isnanto, “Aplikasi Pengenalan Ucapan dengan Ekstraksi ciri MFCC Melalui Jaringan JST untuk Mengoperasikan Kursor Komputer”, Transmisi, vol. 13, no 3, pp.82-86, Jun.2012[Online]. Available : https://doi.org/10.12777/transmisi.13.3.82-86.
Hidayat, Syahroni,”Speech Recognition of KV-Patterned Indonesian Syllable Using MFCC and Hmm”,Kursor. 8.67.10.28961/kursor.v8i2.63.
Hasan, A.I., “Pembangkitan Warna Suara Saron Sintesis Berdasarkan Petikan Senar Gitar”.Available: https://jurnal.ugm.ac.id/ijeis/article/view/15347. [Accessed: 30-May-2021]
Rangga. P.W., “Klasifikasi Tingkat Kemurnian Bahan Bakar menggunakan algoritma K-Nearest Neighbor,” IJEIS (Indonesian J. Electron. Instrum. Syst., Vol.9, No.2, pp. 161~172.2019[Online].Available: https://jurnal.ugm.ac.id/ijeis/article/view/49660/26017 [Accessed: 01-Dec-2021]
S.Faziludeen and P. Sankaran, “ECG Beat Classification Using Evidential K -Nearest Neighbours,” Procedia Comput. Sci., vol. 89, pp. 499–505, 2016 [Online]. Available: http://dx.doi.org/10.1016/j.procs.2016.06.106
Y. F. Safri, R. Arifudin, and M. A. Muslim, “K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor,” Sci. J. Informatics, vol. 5, no. 1, p. 18, 2018.
Martinez, J., Perez, dan Suzuki, M.M, “Speaker Recognition using MFCC and Vector Quantization techniques,” International Conference on Electronics Communications and Computting, vol. 6, no. 1, pp . 248-251 .Available : https : //dx.doi.org/10.1109/CONIELECOMP.2012.61890. [Accesed: 22-June- 2021].
A. Mustofa, “Sistem Pengenalan Penutur dengan Metoode-Mel-frequency Wrapping,’ J. Tek. Elektro, Vol.7, no.2, pp.88-96, 2007[Accesed: 25-Nov-2021]
DOI: https://doi.org/10.22146/ijeis.70898
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