Music Genre Classification Using Machine Learning Techniques

• URL: http://arxiv.org/abs/2509.01762v1
• Date: Mon, 01 Sep 2025 20:43:55 GMT
• Title: Music Genre Classification Using Machine Learning Techniques
• Authors: Alokit Mishra, Ryyan Akhtar,
• Abstract summary: This paper presents a comparative analysis of machine learning methodologies for automatic music genre classification.<n>We evaluate the performance of classical classifiers, including Support Vector Machines (SVM) and ensemble methods, trained on a comprehensive set of hand-crafted audio features.<n>Our findings demonstrate a noteworthy result: the SVM, leveraging domain-specific feature engineering, achieves superior classification accuracy compared to the end-to-end CNN model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comparative analysis of machine learning methodologies for automatic music genre classification. We evaluate the performance of classical classifiers, including Support Vector Machines (SVM) and ensemble methods, trained on a comprehensive set of hand-crafted audio features, against a Convolutional Neural Network (CNN) operating on Mel spectrograms. The study is conducted on the widely-used GTZAN dataset. Our findings demonstrate a noteworthy result: the SVM, leveraging domain-specific feature engineering, achieves superior classification accuracy compared to the end-to-end CNN model. We attribute this outcome to the data-constrained nature of the benchmark dataset, where the strong inductive bias of engineered features provides a regularization effect that mitigates the risk of overfitting inherent in high-capacity deep learning models. This work underscores the enduring relevance of traditional feature extraction in practical audio processing tasks and provides a critical perspective on the universal applicability of deep learning, especially for moderately sized datasets.

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