KLASIFIKASI MUSIK BERDASARKAN AKTIF FREQUENSI MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN)

Hardianto Wibowo

Abstract


 Musik adalah nada dan suara yang disusun sedemikian rupa sehingga menciptakan sebuah karya seni indah dengan mengandung unsur irama, lagu, dan keharmonisan melalui alat maupun suara manusia. Musik juga memiliki pola gelombang yang berbeda-beda setiap jenisnya, dan itu dibedakan kedalam genre musik. Dalam musik dibagi menjadi 15 genre utama, yaitu musik klasik, jas gospel, blues, rhythm and blues, funk, rock, metal / hardcore, electronic, ska / rege/ dub, hip hop / rap /rapcore, pop, latin, county, dan dangdut. Penikmat musik ada yang suka dalam jenis musik tertentu musik tertentu. Dalam penelitian ini akan dilakukan klasifikasi musik berdasarkan aktif frequensi, disebabkan musik memiliki pola gelombang yang berbeda.

Full Text:

PDF

References


Davies, S. (2006). "Artistic Expression and the Hard Case of Pure Music", in: Kieran, M. (Ed.), Contemporary Debates in Aesthetics and the Philosophy of Art: 179-91. P. 181

Nopthaisong, Chakkapong, and Md Maruf Hasan. "Automatic music classification and retreival: Experiments with Thai music collection." Information and Communication Technology, 2007. ICICT'07. International Conference on. IEEE, 2007.

Xu, Changsheng, Namunu Chinthaka Maddage, and Xi Shao. "Automatic music classification and summarization." IEEE transactions on speech and audio processing 13.3 (2005): 441-450.

Lo, Yu-Lung, and Yi-Chang Lin. "Content-based music classification." Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on. Vol. 2. IEEE, 2010.

Lidy, Thomas, Georg Pölzlbauer, and Andreas Rauber. "Sound Re-synthesis from rhythm Pattern Features-audible insight into a Music Feature Extraction Process." ICMC. 2005.

S. Dixon. An interactive beat tracking and visualisation system. In Proc. Intl. Computer Music Conf., Havana, Cuba, 2001.

S. Dixon, E. Pampalk, and G. Widmer. Classification of dance music by periodicity patterns. In Proc. Intl. Conf. on Music Information Retrieval (ISMIR), Baltimore, USA, 2003.

E. Gomez, A. Klapuri, and B. Meudic. Melody description and extraction in the context of music content processing. J. New Music Research, 32(1), 2003.

M. Goto and Y. Muraoka. A real-time beat tracking system for audio signals. In Proc. Intl. Computer Music Conf., Banff, Canada, 1995.

P. Lepain. Polyphonic pitch extraction from musical signals. J. New Music Research, 28(4), 1999.

G. Tzanetakis, A. Ermolinskyi, and P. Cook. Pitch histograms in audio and symbolic music information retrieval. In Proc. Intl. Conf. on Music Information Retrieval (ISMIR), Paris, France, 2002.

Deepa, P. L., and K. Suresh. "An optimized feature set for music genre classification based on Support Vector Machine." Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE. IEEE, 2011.




DOI: https://doi.org/10.22219/sentra.v0i2.1462

Refbacks

  • There are currently no refbacks.


Seketariat

Fakultas Teknik

Universitas Muhammadiyah Malang Kampus III

Jl. Raya Tlogomas 246 Malang, 65144