Comparative study on improved support vector machine and random forest classifier for efficient classification of music genre based on accuracy.

The purpose of this study is to apply machine learning methods to the problem of classifying musical genres. Here, signal processing is used to extract the rhythm pattern feature, which is then used in conjunction with machine learning algorithms to provide a multiclass categorization of musical genres. A preexisting internet music collection is used to generate two distinct datasets, one with undersampling and the other with oversampling to achieve class balance. Two types of models were compared for their efficacy in this research. The first model is an enhanced support vector machine that is fully trained to identify the musical genre of an input signal from its spectrogram. The Random Forest Classifier is the second model. G power for this research is 80%; there are two groups in total, and each has a sample size of 20. Experiments performed on the audio dataset show that an assembler classifier that takes use of both techniques achieves an AUC of 0.894. According to the findings, the support vector machine method provides a higher accuracy (69.24%) than the Random Forest Classifier (64.26%). Independent sample T-test significance level of P=0 (P 0.05, 2-tailed). The findings showed that the accuracy of the music genre classification using the enhanced support vector machine with an unique Rhythm pattern feature method was higher than using the Random Forest Classifier. [ABSTRACT FROM AUTHOR]