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.



Full Text:

PDF


References

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

Article Metrics

Abstract views : 1642 | views : 1478

Refbacks

  • There are currently no refbacks.




Copyright (c) 2022 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis



View My Stats1
View My Stats2