基于隐马尔可夫模型的音乐分类

音乐类型（Genre）是应用最普遍的管理数字音乐数据库的方式，提出一种基于隐马尔可夫模型（Hidden Markov Models，HMMs）的音乐自动分类方案。在考虑传统的音色特征（Timbre）的同时，将另一重要特征节奏（Tempo）也加以考虑，并通过bagging训练两组HMM进行分类，达到了良好的效果。从结构、状态数和混合高斯模型数三个方面进行了参数优化，找到了最佳的HMM参数。在音乐数据集GTZAN上对传统模型和新模型分类效果进行了测试，结果表明考虑了节奏特征的HMM分类效果更佳。

Music classification based on Hidden Markov Models

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.