The coherent organisation of thematic material into large-scale structures within a composition is an important concept in both traditional and cognitive theories of music. However, empirical evidence supporting their perception is scarce. Providing a more nuanced approach, this paper introduces a computational model of hypothesised cognitive mechanisms underlying perception of large-scale thematic structure. Repetition detection based on statistical learning forms the model's foundation, hypothesising that predictability arising from repetition creates perceived thematic coherence. Measures are produced that characterise structural properties of a corpus of 623 monophonic compositions. Exploratory analysis reveals the extent to which these measures vary systematically and independently.

在作品中将主题材料连贯地组织成大规模结构是传统音乐理论和认知音乐理论中的一个重要概念。然而，支持他们看法的经验证据很少。本文提供了一种更细致入微的方法，介绍了一种假设认知机制的计算模型，该模型是大规模主题结构感知的基础。基于统计学习的重复检测构成了模型的基础，假设重复产生的可预测性会产生感知的主题连贯性。产生了表征 623 个单音成分的语料库的结构特性的度量。探索性分析揭示了这些措施系统和独立变化的程度。