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节奏的生成模型

A Generative Model for Rhythms
课程网址: http://videolectures.net/mbc07_paiement_gmr/  
主讲教师: Jean-François Paiement
开课单位: IDIAP研究所
开课时间: 2008-02-01
课程语种: 英语
中文简介:
音乐建模涉及捕捉时间序列中的长期依赖关系,事实证明,传统的统计方法很难实现。 仅考虑节奏时会出现同样的问题。 在本文中,我们基于子序列之间的距离分布引入节奏的生成模型。 描述了在简单节奏表示上考虑汉明距离时模型的具体实现。 在两个不同的音乐数据库上,所提出的模型在条件预测精度方面始终优于标准隐马尔可夫模型。
课程简介: Modeling music involves capturing long-term dependencies in time series, which has proved very difficult to achieve with traditional statistical methods. The same problem occurs when only considering rhythms. In this paper, we introduce a generative model for rhythms based on the distributions of distances between subsequences. A specific implementation of the model when considering Hamming distances over a simple rhythm representation is described. The proposed model consistently outperforms a standard Hidden Markov Model in terms of conditional prediction accuracy on two different music databases.
关 键 词: 音乐建模; 生成模型; 标准隐马尔可夫模型
课程来源: 视频讲座网
最后编审: 2019-05-16:cjy
阅读次数: 72