期刊名称：International Journal of Soft Computing and Software Engineering
电子版ISSN：2251-7545
出版年度：2013
卷号：3
期号：3
页码：581-587
DOI：10.7321/jscse.v3.n3.88
出版社：Advance Academic Publisher
摘要：Music performance conveys profound music understanding and artistic expression in musical sound. These performance-related dimensions can be extracted from audio and encoded as musical expressive features, which is based on a high-dimensional sequential data structure. In this paper we propose a structure learning based method using probabilistic graphical models that obtains a hierarchical dependency graph from musical expressive features. The hierarchical dependency graph we proposed serves as an intuitive visualization interface of the internal dependency patterns within feature data series and helps music scholars identify in-depthconceptual structures.
关键词：knowledge engineering ; feature analysis ; probabilistic graphical model ; music performance analysis
