Automatic Derivation of Musical Structure: A Tool for Research on Schenkerian Analysis
Automatic Derivation of Musical Structure: A Tool for Research on Schenkerian Analysis
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音乐结构的自动推导：申克分析研究的工具
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发表时间：
2007
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通讯作者：
A. Marsden
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作者：
A. Marsden
本文描述了一种软件来促进从音乐表面自动派生层次（申克）音乐结构的研究。许多MIR任务需要有关音乐结构的信息，或者如果这些信息可用，将执行得更好。音乐结构的自动推导面临着两大障碍。首先，一个部件的可能结构分析的解空间非常大。其次，乐曲可以有不止一种有效的结构分析，而音乐理论家对于如何区分一个好的分析几乎没有明确的共识。为了克服第一个障碍，已经开发出了一种软件，它可以从音乐表面（即，类似midi的音符时间信息）派生出易于处理的可能性“矩阵”。矩阵有点像动态规划算法的中间结果，通过从顶层到表层的适当路径，可以以类似的方式从矩阵中提取特定的结构分析。因此，它提供了一种工具，通过允许候选的“好”指标纳入软件并在实际音乐中进行测试，从而促进对第二个障碍的研究。1. 结构的意义
This paper describes software to facilitate research on the automatic derivation of hierarchical (Schenkerian) musical structures from a musical surface. Many MIR tasks require information about musical structure, or would perform better if such information were available. Automatic derivation of musical structure faces two significant obstacles. Firstly, the solution space of possible structural analyses of a piece is very large. Secondly, pieces can have more than one valid structural analysis, and there is little firm agreement among music theorists about how to distinguish a good analysis. To circumvent the first of these obstacles, software has been developed which derives a tractable ‘matrix’ of possibilities from a musical surface (i.e., MIDI-like note-time information). The matrix is somewhat like the intermediate results of a dynamic-programming algorithm, and in a similar way it is possible to extract a particular structural analysis from the matrix by following the appropriate path from the top level to the surface. It therefore provides a tool to facilitate research on the second obstacle by allowing candidate ‘goodness’ metrics to be incorporated into the software and tested on actual music. 1. THE SIGNIFICANCE OF STRUCTURAL