关键词: 分类, 音乐类型, 节奏, 隐马尔可夫模型

Abstract: Music genre is one of the most common ways used in digital music database management. A music automatic classification scheme based on Hidden Markov Models （HMMs） is proposed. While considering traditional timbre, another important feature--Tempo is taken into consideration. Meanwhile, the bagging is used to train two groups of HMM for classification, which obtain good results. In this paper, optimized hyper-parameters in the HMM structure, the number of states and Gaussian mixtures, and find the best HMM parameters. Furthermore, the traditional model and original model are tested on the well-known GTZAN database. The results show that the proposed method considering tempo feature acquires better classification accuracy compared to the traditional model.

Key words: classification, music genre, tempo, Hidden Markov Models