发表机构
National Taiwan University（台湾大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出带显式音符-事件分词与音高有效性约束解码的吉他谱转录框架，在DadaGP和Francois Leduc数据集上提升了吉他谱准确率。
AI 中文摘要
吉他谱转录会为每个音符预测对应的弦和品位置，使生成的吉他谱能重现目标音乐片段。此前的序列到序列方法在大规模数据集上已取得不错效果，但在不同数据集规模下的泛化行为仍有待探索。本研究提出一种吉他谱转录框架，包含显式音符-事件分词与正则化训练。所提出的解码器标记表示融合了音符-事件标记与TAB标记，可更明确地表示音符边界、音高相关事件及弦品位置。我们在大规模数据集DadaGP和小规模数据集Francois Leduc上评估该框架，在DadaGP上，本方法相比基准模型Fretting Transformer提升了吉他谱准确率，直接在小规模Leduc数据集上训练时提升尤为显著。我们还引入音高有效性约束解码策略，生成过程中会掩盖音高无效的TAB候选，而非在解码后修正，同时保留输入的原始时序与音符结构。该约束提升了吉他谱准确率，还提供了可控环境以测量移除音高无效预测后剩余的误差量。我们的代码将在以下网址发布：this https URL
英文摘要
Guitar tablature transcription predicts the string and fret position for each note so that the resulting tablature reproduces the target musical part. Prior sequence-to-sequence approaches have shown promising results on large-scale datasets, but their generalization behavior across different dataset scales remains less explored. In this work, we propose a guitar tablature transcription framework with explicit note-event tokenization and regularized training. The proposed decoder token representation incorporates note-event tokens together with TAB tokens, allowing note boundaries, pitch-related events, and string-fret positions to be represented more explicitly. We evaluate the proposed framework on DadaGP, a large-scale dataset, and Francois Leduc, a small-scale dataset. Our method improves tablature accuracy over the Fretting Transformer baseline on DadaGP, with especially strong gains when trained directly on the small-scale Leduc dataset. We further introduce a pitch-validity constrained decoding strategy that masks pitch-invalid TAB candidates during generation rather than correcting them after decoding and simultaneously preserves the original timing and note structure from the input. This constraint improves tablature accuracy and provides a controlled setting for measuring how much error remains after pitch-invalid predictions are removed. Our code will be released at:https://github.com/MusicGuitarTab/GuitarTab
