Voice leading is a common task in Western music composition whose conventions are consistent with fundamental principles of auditory perception. Here we introduce a computational cognitive model of voice leading, intended both for analyzing voice-leading practices within encoded musical corpora and for generating new voice leadings for unseen chord sequences. This model is feature-based, quantifying the desirability of a given voice leading on the basis of different features derived from Huron’s (2001) perceptual account of voice leading. We use the model to analyze a corpus of 370 chorale harmonizations by J. S. Bach, and demonstrate the model’s application to the voicing of harmonic progressions in different musical genres. The model is implemented in a new R package, “voicer,” which we release alongside this paper.

中文翻译：

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用于分析和生成语音引导的计算认知模型

语音引导是西方音乐创作中的一项常见任务，其惯例与听觉感知的基本原则是一致的。在这里，我们介绍了语音引导的计算认知模型，既用于分析编码音乐语料库中的语音引导实践，也用于为看不见的和弦序列生成新的语音引导。该模型是基于特征的，根据 Huron (2001) 对语音引导的感知描述得出的不同特征，量化给定语音引导的可取性。我们使用该模型分析了 JS Bach 的 370 首合唱和声语料库，并展示了该模型在不同音乐流派的和声进行中的应用。该模型在我们与本文一起发布的新 R 包“voicer”中实现。