Problem
Research questions and friction points this paper is trying to address.
Natural-Language Exploration
Symbolic Music Scores
Music Analysis
ScorePrompts: Natural-Language Exploration of Symbolic Music Scores through Analysis
🤖 AI Summary
ScorePrompts系统通过分析乐谱的音乐结构并以自然语言描述，解答用户关于特定段落的问题，使用了MIR组件和受限语言模型等方法。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
natural-language exploration
symbolic music analysis
specialist MIR components
schema-constrained language model
interactive system
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