Performance Evaluation of Some Machine Learning Models for Music Genre Classification

Abstract

Abstract: Music genre classification is a challenging task in the field of music information retrieval due to the overlapping characteristics of certain genres and the variability in audio quality. Several techniques have been developed to accurately classify music genre. However, these techniques have not been adequately analysed and compared. Hence, this study investigates the comparative performance of Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest (RF) in music genre classification. Mel-Frequency Cepstral Coefficients (MFCCs) were extracted from the audio samples using the Librosa library. Next, the three machine learning models - Convolutional Neural Network (CNN), Support Vector Machine (SVM) and Random Forest (RF) - were trained. The CNN model was designed with multiple convolutional and pooling layers, along with dropout for regularization. The SVM model was used to create an optimal hyperplane for classification, while the RF model utilized an ensemble of decision trees. Finally, the models were evaluated and compared using accuracy, precision, recall and F1 score. The results of the evaluation and comparism indicate that CNN achieved 95% accuracy, 93% precision, 92% recall and 91% F-1 score. SVM achieved 93% accuracy, 90% precision, 80% recall and 70% F-1 score while RF achieved 77% accuracy, 77% precision, 72% recall and 60% F-1 score. The result demonstrated that CNN outperformed SVM and RF interms of accuracy, precision, recall, and F-1 score: CNN is thereby recommended for Music Genre Classification, this finding underscore the efficiency of CNN addressing the challenges task in the field of music information retrieval and leading to the advancement of automated music classification system and improve the accessibility and enjoyment of digital music libraries.