计算机工程与应用2017，Vol.53Issue(16)：138-143,165,7.DOI:10.3778/j.issn.1002-8331.1603-0441
基于隐马尔可夫模型的音乐分类
Music classification based on Hidden Markov Models.
摘要
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 origi-nal 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.关键词
分类/音乐类型/节奏/隐马尔可夫模型Key words
classification/music genre/tempo/Hidden Markov Models分类
信息技术与安全科学引用本文复制引用
肖晓红,张懿,刘冬生,欧阳春娟..基于隐马尔可夫模型的音乐分类[J].计算机工程与应用,2017,53(16):138-143,165,7.基金项目
国家自然科学基金(No.61462046) （No.61462046）
江西省教育厅科学技术研究项目(No.GD14559). （No.GD14559）
