Klasifikasi Genre Musik Menggunakan Support Vector Machine Berdasarkan Spectral Features
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
This work is licensed under a Creative Commons Attribution 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, the copyright of the article shall be assigned to JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) as the publisher of the journal. Copyright encompasses exclusive rights to reproduce and deliver the article in all forms and media, as well as translations. The reproduction of any part of this journal (printed or online) will be allowed only with written permission from JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya). The Editorial Board of JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) makes every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal.
This work is licensed under a Creative Commons Attribution 4.0 International License.
