Active Learning Music Genre Classification Based on Support Vector Machine

Abstract

The improved SVM (support vector machine) offers an active training method that provides users with the most informative sample through multiple iterations and adds it to the training package, which can significantly reduce the cost of manually labeling samples. To evaluate the classifier’s performance, 801 music samples were tested for five music types (Dance, Lyric, Jazz, Folk, and Rock). The effectiveness of the proposed SVM active training method was confirmed by two things: the convergence speed and the classification accuracy, and the number of samples to be labeled with the same accuracy. And the classification accuracy was 81%. At the expense of a little precision, both SVM active training methods drastically reduce the number of labels to be trained, and the method proposed in this paper works better. At the same time, the smaller the value, the fewer the labels that need to be labeled. This is because increasing the number of iterations allows the classifier to select the most appropriate sampling points, while the larger the set value, the smaller the number of iterations. So you can choose between the two depending on the actual situation.