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Compiling Hierarchical Dependency Graph for Large-Span Musical Expressive Feature Analysis Using Multi-Scaling Probabilistic Graphical Models  
Gang Ren1,Xuchen Yang2,Zhe Wen3,Dave Headlam4,Mark Bocko5
*1, University of Rochester, Email : g.ren@rochester.edu
2, University of Rochester, Email : xuchenyang@rochester.edu
3, University of Rochester, Email : zhe.wen@rochester.edu
4, University of Rochester, Email : dheadlam@esm.rochester.edu
5, University of Rochester, Email : mark.bocko@rochester.edu
 
Abstract .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.
 
Keywords : knowledge engineering ; feature analysis ; probabilistic graphical model ; music performance analysis
 URL: http://dx.doi.org/10.7321/jscse.v3.n3.88  
 
 

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