Klasifikasi Genre Musik Menggunakan Support Vector Machine Berdasarkan Spectral Features

  • I Gusti Agung Ngurah Diputra Wiraguna Universitas Udayana
  • Luh Arida Ayu Rahning Putri Universitas Udayana

Abstract

This research focuses on music genre classification based on spectral features and Support Vector Machine (SVM). Features such as Spectral Centroid, Spectral Rolloff, Spectral Flux, and Spectral Bandwidth are extracted from MP3 music audio. The dataset comprising 4 music genres is utilized for training and testing the system. The extracted spectral features are fed into the SVM classifier to predict the genre of test samples. Python and machine learning are both used in developing the system while the experimental results demonstrate the effectiveness of SVM in accurately classifying music genres based on the current extracted features. The proposed approach contributes to automated music genre classification systems, facilitating music organization, recommendation, and retrieval. This research promotes advancements in music information retrieval and enhances user experience in music-related applications.


Keywords: Music Feature Extraction, MP3, Music, Spectral Features, SVM

Published
2023-07-17
How to Cite
DIPUTRA WIRAGUNA, I Gusti Agung Ngurah; PUTRI, Luh Arida Ayu Rahning. Klasifikasi Genre Musik Menggunakan Support Vector Machine Berdasarkan Spectral Features. Jurnal Nasional Teknologi Informasi dan Aplikasnya, [S.l.], v. 1, n. 3, p. 933-940, july 2023. ISSN 3032-1948. Available at: <https://ojs.unud.ac.id/index.php/jnatia/article/view/103526>. Date accessed: 31 jan. 2025.
Citation Formats
Issue
Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Section
Articles

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