Music Genre Classification Using Machine Learning

Abstract

Music genre classification is a subfield of audio and music analysis in which machine learning and data analysis techniques are used to automatically categorize music tracks into predefined genre categories. In this study, we explore music genre classification using three machine learning algorithms: Support Vector Machine (SVM), Naive Bayes, and k-Nearest Neighbors (k-NN). Our dataset spans diverse music genres, from mainstream to niche, and we employ feature extraction techniques like rhythm-based features. Evaluation metrics, including accuracy, precision, recall, and F1-score, assess model performance. Cross-validation ensures robustness, while addressing imbalanced data is considered. Our findings offer insights into the suitability of SVM, Naive Bayes, and k-NN for music genre classification, providing valuable guidance for audio analysis practitioners. This research sets the stage for further exploration of advanced modeling techniques and real-world challenges in audio classification.