用最短路径算法自动简化旋律，更准确且更符合音乐逻辑。
Automatic Melody Reduction via Shortest Path Finding
- 将旋律简化建模为图中的最短路径问题，利用音乐理论指导搜索。
- 在流行、民谣和古典乐上表现优于传统下采样方法，保真度与连贯性更高。
- 适合音乐生成、变奏创作等下游任务，可提升生成质量。
旋律简化作为音乐作品的抽象表示，不仅用于音乐分析，也是结构化音乐生成的中间表示。以往的计算理论（如调性音乐生成理论）虽具启发性，但缺乏自动化且多限于古典音乐。本文提出一种新颖而简洁的计算方法，基于图结构表示，受计算音乐理论启发，将旋律简化转化为最短路径查找问题。我们在流行、民谣和古典三个音乐类型上评估该算法，实验结果表明，其生成的旋律简化更贴近原旋律，且音乐连贯性更强，优于常见下采样方法。作为下游应用，我们使用旋律简化生成符号音乐变奏，实验显示本方法在生成质量上超过当前最先进的风格迁移技术。
原文摘要 · Abstract (English)
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.
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