用十六进制编码改进音乐记谱，让大模型更好生成江南民歌。
HNote: Extending YNote with Hexadecimal Encoding for Fine-Tuning LLMs in Music Modeling
- 基于YNote设计十六进制音乐记谱系统，统一音高与时长表示。
- 在12,300首江南风格曲目上微调8B参数模型，语法正确率达82.5%。
- 适合对传统文化音乐生成、符号化建模感兴趣的开发者与研究者。
大型语言模型（LLMs）的进展为符号化音乐生成带来了新机遇。然而，现有格式如MIDI、ABC和MusicXML或过于复杂，或结构不一致，限制了其在基于令牌的学习架构中的适用性。为此，我们提出HNote，一种从YNote扩展而来的新型十六进制记谱系统，将音高与持续时间编码于固定32单位度量框架内。该设计确保结构对齐，减少歧义，并直接兼容LLM架构。我们将12,300首源自传统民谣的江南风格歌曲从YNote转换为HNote，使用参数高效方法LoRA对LLaMA-3.1(8B)进行微调。实验表明，HNote实现82.5%的语法正确率，且BLEU与ROUGE评估显示其具有强符号与结构相似性，生成作品风格一致。本研究确立了HNote作为大模型与文化音乐建模融合的有效框架。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have created new opportunities for symbolic music generation. However, existing formats such as MIDI, ABC, and MusicXML are either overly complex or structurally inconsistent, limiting their suitability for token-based learning architectures. To address these challenges, we propose HNote, a novel hexadecimal-based notation system extended from YNote, which encodes both pitch and duration within a fixed 32-unit measure framework. This design ensures alignment, reduces ambiguity, and is directly compatible with LLM architectures. We converted 12,300 Jiangnan-style songs generated from traditional folk pieces from YNote into HNote, and fine-tuned LLaMA-3.1(8B) using parameter-efficient LoRA. Experimental results show that HNote achieves a syntactic correctness rate of 82.5%, and BLEU and ROUGE evaluations demonstrate strong symbolic and structural similarity, producing stylistically coherent compositions. This study establishes HNote as an effective framework for integrating LLMs with cultural music modeling.
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