Abstract
Melody reduction, as an abstract representation of musical compositions, serves not only as a tool for music analysis but also as an intermediate representation for structured music generation. Prior computational theories, such as the Generative Theory of Tonal Music, provide insightful interpretations of music, but they are not fully automatic and usually limited to the classical genre. In this paper, we propose a novel and conceptually simple computational method for melody reduction using a graph-based representation inspired by principles from computational music theories, where the reduction process is formulated as finding the shortest path. We evaluate our algorithm on pop, folk, and classical genres, and experimental results show that the algorithm produces melody reductions that are more faithful to the original melody and more musically coherent than other common melody downsampling methods. As a downstream task, we use melody reductions to generate symbolic music variations. Experiments show that our method achieves higher quality than state-of-the-art style transfer methods.
Abstract (translated)
旋律简化作为一种音乐作品的抽象表示，不仅作为音乐分析的工具使用，还作为结构化音乐生成的中间表达。此前的计算理论，如《调性音乐生成理论》，提供了对音乐深入的解读，但它们并非完全自动化，并且通常仅限于古典音乐领域。在本文中，我们提出了一种新颖而概念简单的基于图表示的旋律简化计算方法，该方法受计算音乐理论原理启发，将简化过程定义为寻找最短路径问题。我们在流行、民间和古典音乐等多种类型上评估了我们的算法，并通过实验结果证明，与常用的旋律降采样方法相比，该算法生成的旋律简化更忠实于原始旋律且更具音乐一致性。作为下游任务，我们利用简化后的旋律来生成符号音乐变体。实验表明，我们的方法比现有的最先进的风格转换方法产生了更高品质的结果。
URL
https://arxiv.org/abs/2508.01571
