ONOTE：面向专家级音乐智能的多模态记谱处理基准测试
ONOTE: Hypergraph-Grounded Omnimodal Reasoning for Computational Music Science
AI总结：
本文提出ONOTE基准测试，通过确定性流程消除记谱系统偏差，揭示多模态模型在感知准确性和音乐理论理解间的根本分歧。
摘要：
多模态记谱处理（ONP）因需在听觉、视觉和符号领域进行严格多维对齐而成为多模态AI的独特前沿。当前研究碎片化，聚焦于孤立转录任务，未能弥合表层模式识别与底层音乐逻辑间的差距。此领域进一步复杂化于对西方谱号的严重偏见以及'LLM-as-a-judge'指标的内在不可靠性，常掩盖结构性推理失败。为建立更严谨的标准，我们引入ONOTE，一种多格式基准，利用以标准音高投影为基础的确定性流程，消除不同记谱系统中的主观评分偏差。对领先多模态模型的评估揭示了感知准确性和音乐理论理解间的根本断层，为诊断复杂、规则约束领域中的推理漏洞提供了必要框架。
英文摘要：
Omnimodal notation processing, centered on sheet music, is a controlled scientific setting in which auditory, visual, symbolic, and physical representations must encode the same musical events. Yet existing work remains fragmented across recognition and transcription, rarely testing structural consistency across notation systems. Western-staff bias and underspecified model judges further conceal errors in pitch, timing, ordering, and instrument-specific constraints. We introduce ONOTE, a unified framework that treats music as a scientifically structured domain of measurable cross-representation correspondences. Its test-only benchmark draws on a diverse collection of musical sources covering staff, Jianpu, and tablature across varied genres, instruments, and structural conditions, with aligned multimodal derivatives. Four complementary tasks cover score understanding, notation conversion, audio transcription, and symbolic generation, testing pitch and duration ordering, output syntax, and disclosed instrument-specific constraints. ONOTE also constructs a provenance-bearing proposition hypergraph from external music-theory materials for entity- and hyperedge-based evidence retrieval. Deterministic validity checks, disclosed structural-compliance SMG scoring, and controlled RAG comparisons reveal gaps between visual recognition and structure-preserving outputs. Results separate perception from music-theory application and structural or physical constraint satisfaction. ONOTE provides an auditable framework for studying representation invariance and knowledge-grounded intervention in computational music science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
