Unsupervised classification of music genre using hidden Markov model
Music genre classification can be of great utility to musical database management. Most current classification methods are supervised and tend to be based on contrived taxonomies. However, due to the ambiguities and inconsistencies in the chosen taxonomies, these methods are not applicable for a much larger database. We proposed an unsupervised clustering method, based on a given measure of similarity which can be provided by hidden Markov models. In addition, in order to better characterize music content, a novel segmentation scheme is proposed, based on music intrinsic rhythmic structure analysis and features are extracted based on these segments. The performance of this feature segmentation scheme performs better than the traditional fixed-length method, according to experimental results. Our preliminary results also suggest that the proposed method is comparable to the supervised classification method.