ScorePrompts：通过分析对符号音乐乐谱进行自然语言探索
ScorePrompts: Natural-Language Exploration of Symbolic Music Scores through Analysis
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中文总结 AI 辅助
ScorePrompts是一个交互式系统，通过专业MIR组件和模式约束语言模型，将符号音乐乐谱分析结果转化为自然语言描述，支持问答检索和可视化，用于探索性乐谱分析。
中文摘要 AI 辅助
我们提出了ScorePrompts，一个交互式系统，用户在其中上传乐谱，接收对其音乐结构的自然语言描述，询问关于特定段落的问题，并在五线谱记谱法中检查相应的分析结果。专业音乐信息检索（MIR）组件首先估计和声、调性、终止式、形式边界、织体以及音符级角色，并将其输出组织在音符、节拍、小节和乐曲级别。一个受模式约束的语言模型将这些结果转换为描述，而不是直接从原始MusicXML中推断音乐结构。对于诸如“第14-18小节发生了什么变化？”这样的问题，一个确定性路由器选择相关的小节和分析级别，并返回一个简洁的响应以及底层结果和注意事项。Verovio渲染乐谱，并将返回的信息链接到引用的小节和音符级属性。该界面还暴露中间表格和分析级别之间的不一致。ScorePrompts旨在用于探索性乐谱分析和解释，而非乐谱编辑。演示展示了现有的分析模型、受约束的语言生成、问答检索和基于记谱法的可视化如何提供对符号音乐分析的自然语言访问，同时保持中间结果可检查。
英文摘要
We present ScorePrompts, an interactive system in which users upload a score, receive natural-language descriptions of its musical structure, ask questions about specific passages, and inspect the corresponding analysis results in staff notation. Specialist MIR components first estimate harmony, tonality, cadences, formal boundaries, texture, and note-level roles, organizing their outputs at note, beat, measure, and piece levels. A schema-constrained language model converts these results into descriptions rather than inferring musical structure directly from raw MusicXML. For questions such as "What changes in measures 14-18?", a deterministic router selects the relevant measures and analytical levels and returns a concise response together with the underlying results and caveats. Verovio renders the score and links the returned information to cited measures and note-level attributes. The interface also exposes intermediate tables and disagreements between analytical levels. ScorePrompts is intended for exploratory score analysis and explanation, not score editing. The demo shows how existing analysis models, constrained language generation, Q&A retrieval, and notation-based visualization can provide natural-language access to symbolic music analysis while keeping intermediate results inspectable.
