2025/09/01 by Mishra, Alokit, Akhtar, Ryyan
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Sound (cs.SD)
paper · doi:10.48550/arxiv.2509.01762
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.