基于确定性图行走的音阶识别

Andres Eduardo Coca Salazar, Liang Zhao
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引用次数: 0

摘要

音阶在旋律中起着重要的作用,音阶的性质反映了旋律的本质。音阶的提取和理解在音乐的分析和创作中都是必不可少的。然而,尺度识别是一项艰巨的任务。因此,经典的识别音阶的算法是基于最流行的音阶,如大调音阶和小调音阶。在本文中,我们提出了一种识别音阶的综合方法,该方法可以检测传统音阶之外的广泛音阶。我们的方法使用确定性遍历图的节点,其中每个节点表示一个有效的区间结构。节点之间的转换遵循控制间隔碎片的验证规则执行。此外,如果尺度不完整,则可以根据谐波相似百分比度量确定可能的结构并估计尺度。所提出的方法已经使用芬兰民间旋律数据库和使用很少使用的音阶组成的随机旋律数据集进行了测试。实验结果表明,该方法具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Musical Scales Recognition via Deterministic Walk in a Graph
Musical scales play an important role in melodies, since its properties are reflected to the melodic essence. The extraction and understanding of scales are essential in both analysis and composition of music. However, the scale identification is a nontrivial task. Consequently, classic algorithms for identifying scales have been developed based on the most popular scales, such as major and minor scales. In this paper, we propose a comprehensive method for identifying musical scales, which allows to detect a wide range of scales beyond the traditional ones. Our method uses a deterministic walk through the nodes of a graph, where each node represents a valid interval structure. The transition between nodes is performed following a validation rule that governs the fragmentation of intervals. Moreover, if the scale is incomplete, possible structures can be determined and the scale is estimated according to the harmonic similarity percentage measure. The proposed method has been tested using a database of Finnish folk melodies and a data set of random melodies composed using rarely used scales. Experimental results show good performance of the proposed technique.
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