Music Genre Classi cation Using Gaussian Process Models and Gibbs samplers
Music genres are groups with certain musical characteristics. They can be used to create structure within the large amount of music that exists, for example via genre classi cation. Genre classi cation has been automated using Machine Learning. In previous research, Gaussian Processes have successfully been used to classify genres with a reasonable accuracy (60,9% over 6 genres), using relatively few samples (20 per genre). In this paper, several genre classi cation approaches are compared: a Gaussian Process, a Gibbs sampler and as a baseline a Support Vector Ma- chine. First, the optimal kernel for both the Gaussian Process and Support Vector Machine is determined. Then, the models are compared. The models are trained with a range of di erent sample sizes and accuracy is determined using the same testing set. The accuracies that the models achieve are then compared. Using the Support Vector Machine with the polynomial kernel yielded the highest accuracies with the polynomial kernel and the Gaussian Process achieved the highest accuracies with the Mat ern kernel. When trained on the same data, the Gaussian Process outperforms the Support Vector Machine. The Gaussian Process reaches an accuracy of 73,1%, while the Support Vector Machine reaches an accuracy of 61,7%. The Gibbs sampler proves to perform relatively poor on this classi cation problem, as it yields an accuracy of 48,2%.
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