Artificial Intelligence in Music Genre Classification: A Systematic Review of Techniques, Applications, and Emerging Trends
Rachael A Oladejo, Adebayo Adewumi Abayomi-Alli, Oluwasefunmi 'Tale Arogundade, Adetunji A Adeyanju, Morolake Oladayo Lawrence · Cureus Journal of Computer Science. · 2025
Music has been an important means of expression and communication for centuries, and its genre classification is integral to its understanding, appreciation, and production. This meta-analysis evaluates the current cutting-edge approaches to music genre classification using a range of evaluation parameters: data sources, methods/techniques, approaches, study status, dataset, and place of publication. A total of 11 electronic databases were thoroughly searched, and 84 relevant articles were selected for closer examination and analysis. A quantitative review was conducted to pinpoint recent insights, measures, methods, and applications in this field, focusing on research published over the last seven years. This research reveals a significant insight: most music genre classification systems are still in the evaluation stage. This study further contributes to the field by developing a taxonomy of state-of-the-art methodologies. Researchers must explore other methods and algorithms for music genre classification to enhance further classification. The future recommendation is to exploit more deep learning techniques due to their unique features and increase the number of datasets used for African music classification, hence improving the performance of classifiers and reducing computational complexity.