Music is the art of combining frequencies. A balance of frequencies gives rise to a harmonious tone. Several features of music can be analyzed, and they include sociocultural background, lyrics, mood, tempo, rhythm, harmony, melody, timbre, and instrumentation. In this study, we use the frequency of instrumentation as a feature for classification because each instrument has a frequency range. To test this frequency range, we use five music genres and one music playing skill. The five genres are dangdut, electronic dance music (EDM), metal, pop/rock, and reggae. The music playing skill is acoustic. Active frequencies are tested using the k-nearest neighbor method, and the results serve as basis of the accuracy of music classification. The classification accuracy for EDM, metal, and acoustic is over 70%, whereas that for dangdut, pop/rock, and reggae is less than 60%. In sum, the accuracy of music classification is influenced by the similarities in the music instruments used and the tempo.

Bahasa Abstract

Pengambilan Informasi Musik Berdasarkan pada Frekuensi Aktif. Musik adalah seni menggabungkan frekuensi. Suatu keseimbangan frekuensi menghasilkan peningkatan sampai suatu nada harmonis. Beberapa fitur musik dapat dianalisis, dan fitur-fitur tersebut mencakup latar belakang sosial budaya, lirik, suasana hati, tempo, ritme, harmoni, melodi, warna nada (timbre), dan instrumentasi. Dalam kajian ini, kami menggunakan frekuensi instrumentasi sebagai suatu fitur untuk pengklasifikasian karena masing-masing instrumen memiliki suatu kisaran frekuensi. Untuk menguji kisaran frekuensi ini, kami menggunakan lima genre musik dan satu keahlian bermain musik. Kelima genre tersebut adalah dangdut, musik tarian elektronik (electronic dance music (EDM), metal, pop/rock, dan reggae. Keahlian bermain musik tersebut adalah akustik. Frekuensi aktif diuji dengan menggunakan metode tetangga terdekat – k, dan hasil-hasilnya berfungsi sebagai basis akurasi klasifikasi musik. Akurasi klasifikasi untuk EDM, metal, dan akustik melampaui 70%, sedangkan untuk dangdut, pop/rock, dan reggae kurang dari 60%. Dalam penjumlahan, akurasi klasifikasi musik dipengaruhi oleh keserupaan dalam instrumen musik yang digunakan dan tempo.


L. Ferrara, (Ed.), Philosophy and the analysis of music: Bridges to musical sound, form and reference, Greenwood Press, 1991.

S. Davies, (Ed.), Artistic expression and the hard case of pure music, 2006.

C. Nopthaisong, M.M. Hasan, ICICT 2007: Proceedings of International Conference on Information and Communication Technology, 2007.

C. Xu, N.C. Maddage, X. Shao, IEEE Trans. Speech Audio Process. 13 (2005) 441.

Y.L. Lo, Y.C. Lin, Proceedings-2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010, 2010, p.112.

T. Lidy, G. Pölzlbauer, A. Rauber, ICMC. (2005).

G. Li, J. Zhang, 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2018, p.777.

N.-H. Liu, Y.-H. Wu, A.L.P. Chen, Multimed. Syst. 10 (2005) 513.

S. Dixon, ICMC, 2001.

M. Goto, Y. Muraoka, ICMC, 1995.

S. Dixon, E. Pampalk, G. Widmer, Classification of dance music by periodicity patterns, 2003.

P. Lepain, J. New Music Res. 28 (1999) 296.

G. Tzanetakis, A. Ermolinskyi, P. Cook, J. New Music Res. 32 (2003) 143.



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