Comparison of improved support vector machine and multilayer perceptron for the prediction of music genre with improved classification accuracy

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Abstract
The capacity of Improved Support Vector Machine and Multilayer Perceptron to automatically identify song snippets into various musical genres is compared in this study. To balance the classes, under sampling and oversampling are used to build two separate datasets from an existing internet music collection. We compare the performance of two types of models in this study, including an Improved support vector machine (SVM) that is trained end to end to predict the genre label of an audio signal simply based on its spectrogram. The Multilayer Perceptron is the second model (MLP). This study has two groups, each with a sample size of 20 people and a G power of 80%. The studies were carrie d out on the audio set, and the AUC value for an assembler classifier that combined the two techniques was 0.894. The results show that the support vector machine algorithm has a 69.24 percent accuracy and exceeds the multilayer perceptron, which has a 66.24 percent accuracy. An independent sample T test yielded a statistical significance of P = 0.001 (P 0.05) with a 2 - tailed significance of P = 0.001. The results showed that the upgraded support vector machine with a novel feature extraction strategy for music genre categorization outperformed the multilayer perceptron approach in terms of accuracy.

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