Semi-Supervised Music Genre Classification

Music genre classification is a hot topic in pattern recognition and signal processing. Classical supervised methods need lost of labeled music data to train a classifier. In this paper, we propose a semi-supervised genre classification algorithm which is developed on several labeled music tracks and lots of unlabelled tracks. Three features are extracted from the each music track and manifold regularization method is used to design the classifier. Experiments on a large number of test music data show that semi-supervised method can improve the classification accuracy.