Visualising Singing Style under Common Musical Events Using Pitch-Dynamics Trajectories and Modified TRACLUS Clustering

Kin Wah Edward Lin, Hans Anderson, Natalie Agus, C. So, Simon Lui
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引用次数: 5

Abstract

We present a novel method for visualising the singing style of vocalists. To illustrate our method, we take 26 audio recordings of A cappella solo vocal music from two different professional singers and we visualise the performance style of each vocalist in a two-dimensional space of pitch and dynamics. We use our own novel modification of a trajectory clustering algorithm called TRACLUS to generate four representative paths, called trajectories, in that two dimensional space. Each trajectory represents the characteristic style of a vocalist during one of four common musical events: (1) Crescendo, (2) Diminuendo, (3) Ascending Pitches and (4) Descending Pitches. The unique shapes of these trajectories characterize the singing style of each vocalist with respect to each of these events. We present the details of our modified version of the TRACULUS algorithm and demonstrate graphically how the plots produced indicate distinct stylistic differences between singers. Potential applications for this method include: (a) automatic identification of singers and automatic classification of singing styles and (b) automatic retargeting of performance style to add human expression to computer generated vocal performances and allow singing synthesisers to imitate the styles of specific famous professional vocalists.
基于音高动态轨迹和改进TRACLUS聚类的常见音乐事件下的歌唱风格可视化
我们提出了一种新的方法来可视化歌手的演唱风格。为了说明我们的方法,我们从两个不同的专业歌手那里获得了26段无伴奏合唱独奏音乐的录音,我们在音高和动态的二维空间中可视化每个歌手的表演风格。我们使用我们自己对轨迹聚类算法的新颖修改,称为TRACLUS,在二维空间中生成四条具有代表性的路径,称为轨迹。每个轨迹代表了一个歌手在四个常见的音乐事件中的一个的特征风格:(1)渐强,(2)渐弱,(3)升调和(4)降调。这些轨迹的独特形状表征了每个歌手与这些事件相关的演唱风格。我们提出了我们修改版本的TRACULUS算法的细节,并以图形方式展示了产生的情节如何表明歌手之间的明显风格差异。该方法的潜在应用包括:(a)歌手的自动识别和演唱风格的自动分类;(b)表演风格的自动重新定位,在计算机生成的声乐表演中加入人类的表达,并允许唱歌合成器模仿特定著名专业歌手的风格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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