AI 中文总结
针对自动符号音乐分析系统单一模式问题，提出统一框架，结合预训练表示在准确性与交互响应性间权衡，支持多种分析操作，实验验证其有效性，为音乐分析交互式工具奠定基础。
AI 中文摘要
自动符号音乐分析已取得重大进展，但现有系统通常专为单一使用模式设计，无法匹配分析工作流程中的多种操作。我们提出了一个统一框架，通过结合强大的预测性能与对受限完成和修订的直接支持来缩小差距。该方法通过一次性计算昂贵的预训练表示并在迭代细化中重用它们，在准确性和交互响应性之间进行实用权衡。实验表明该方法是强大的罗马数字分析基线，还支持从部分标签进行掩码完成，为未来音乐分析交互式工具奠定基础。
英文摘要
Automatic symbolic music analysis has made substantial progress, yet existing systems are typically designed for a single mode of use, such as full-score prediction, and therefore do not match the broader range of operations that arise in analysis workflows, including partial completion, local correction, and iterative refinement. As a result, there remains a gap between strong benchmark models and systems that can support interactive analytical use. We present a unified framework for symbolic Roman-numeral (RN) analysis that narrows this gap by combining strong predictive performance with direct support for constrained completion and revision. The method is designed to provide a practical trade-off between accuracy and interactive responsiveness by computing expensive pretrained representations once and reusing them during iterative refinement, making powerful pretrained models more amenable to interactive settings. It supports complete score analysis, targeted revision of existing labels, and inference of missing annotations from partial context through a shared modeling framework. Experiments on Dilemmadata, the largest and most heterogeneous benchmark of its kind, show that the proposed approach is a strong RN-analysis baseline while also supporting masked completion from partial labels. Together with a prototype interface for multi-level candidate inspection and editing, these results position automatic RN analysis not only as a prediction problem, but also as a foundation for future interactive tools for music analysis.
Commentsin Proceedings of the 27th International Society for Music Information Retrieval Conference (ISMIR) 2026
