Abstract
Motifs often recur in musical works in altered forms, preserving aspects of their identity while undergoing local variation. This paper investigates how such motivic transformations occur within their musical context in symbolic music. To support this analysis, we develop a probabilistic framework for modeling motivic transformations and apply it to Beethoven's piano sonatas by integrating multiple datasets that provide melodic, rhythmic, harmonic, and motivic information within a unified analytical representation. Motif transformations are represented as multilabel variables by comparing each motif instance to a designated reference occurrence within its local context, ensuring consistent labeling across transformation families. We introduce a multilabel Conditional Random Field to model how motif-level musical features influence the occurrence of transformations and how different transformation families tend to co-occur. Our goal is to provide an interpretable, distributional analysis of motivic transformation patterns, enabling the study of their structural relationships and stylistic variation. By linking computational modeling with music-theoretical interpretation, the proposed framework supports quantitative investigation of musical structure and complexity in symbolic corpora and may facilitate the analysis of broader compositional patterns and writing practices.
Abstract (translated)
主题常以变化形式在音乐作品中反复出现，在保持其身份特征的同时经历局部变异。本文研究符号音乐中此类主题变换如何在音乐语境中发生。为支持该分析，我们构建了一个用于模拟主题变换的概率框架，并将其应用于贝多芬钢琴奏鸣曲，通过整合提供旋律、节奏、和声及主题信息的多个数据集，形成统一的分析表征。通过将每个主题实例与其局部语境中的指定参考出现进行比较，主题变换被表示为多标签变量，从而确保变换类别间标签的一致性。我们引入多标签条件随机场来建模主题层面的音乐特征如何影响变换的发生，以及不同变换类别如何倾向于共现。我们的目标是提供一种可解释的、分布式的主题变换模式分析，以研究其结构关系与风格变异。通过将计算建模与音乐理论阐释相结合，所提出的框架支持对符号语料库中音乐结构与复杂性的量化研究，并可能促进对更广泛作曲模式与写作实践的分析。
URL
https://arxiv.org/abs/2603.26478
