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All Journal International Journal of Computer Science and Humanitarian AI
Achmad Reyfanza
Bina Nusantara University
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Systematic Literature Review of The Use of Music Information Retrieval in Music Genre Classification M. Aqila Budyputra; Achmad Reyfanza; Alexander Agung Santoso Gunawan; Muhammad Edo Syahputra
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.13019

Abstract

Emphasizing deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this article explores the application of Music Information Retrieval (MIR) techniques in music genre categorization. These algorithms outperform traditional methods in capturing complex audio patterns, showcasing their potential in advancing music classification tasks. Accurate genre classification critically depends on key features such as spectral, temporal, and timbral characteristics, which play a pivotal role in distinguishing musical styles. However, the performance of these models is heavily influenced by the quality and diversity of the training datasets. Additionally, challenges like model interpretability and reliance on large datasets are addressed. This research utilized a Systematic Literature Review (SLR) to investigate the capabilities of advanced MIR techniques in enhancing music categorization systems, particularly for educational applications and personalized music recommendations. The findings reveal that analyzing the importance of spectral, temporal, and timbral features—key components of MIR—can significantly boost the accuracy and reliability of music genre classification.
Co-Authors Alexander Agung Santoso Gunawan M. Aqila Budyputra Muhammad Edo Syahputra