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.

翻译：音乐动机常在作品中以变形形式复现，在保留其身份特征的同时进行局部变化。本文探究符号音乐中此类动机变换在其音乐语境中的发生机制。为支持该分析，我们开发了一个概率框架用于建模动机变换，并将其应用于贝多芬钢琴奏鸣曲，通过整合多个数据集，将旋律、节奏、和声及动机信息融入统一分析表征。通过将每个动机实例与其局部语境中指定的参考实例进行对比，动机变换被表示为多标记变量，从而确保跨变换族的一致性标记。我们引入多标记条件随机场模型，以刻画动机级音乐特征如何影响变换的发生，以及不同变换族之间的共现倾向。本研究旨在提供关于动机变换模式的可解释性分布分析，从而支持对其结构关系与风格变异的研究。通过将计算建模与音乐理论阐释相结合，该框架支持对符号语料库中音乐结构与复杂性的定量探究，并可能促进对更广泛作曲模式与写作实践的分析。