发表机构
Univ. Bordeaux; CNRS; Bordeaux INP; LaBRI（波尔多大学; 法国国家科学研究中心; 波尔多国立综合理工学院; 计算机科学、图像与智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对吉他指板谱转录的可演奏性问题，提出扩散模型Noise2Fret，引入五种辅助损失优化训练目标，在GuitarSet和GOAT数据集上实现优于基线的性能与计算效率。
AI 中文摘要
吉他指板谱转录不仅需要准确的音高检测，还需为每个音符分配特定的弦-品位置，因为同一音高可在指板的多个品位置演奏。现有方法将此视为标准分类问题，忽略了支配可演奏指法序列的音乐和物理约束。我们提出Noise2Fret，这是一种用于音频到指板谱转录的扩散模型，通过离散品和弦目标的连续潜在表示生成指板谱，以频谱和音频特征为条件。为弥合音高准确性与物理可演奏性之间的差距，我们引入五种辅助损失，将音类距离、位置距离、五度圈距离、弦相似度和手跨度可行性直接编码到训练目标中。在GuitarSet和GOAT数据集上的实验表明，该模型优于基线模型，同时计算效率更高，且辅助损失相比标准训练目标能带来持续的性能提升。
英文摘要
Guitar tablature transcription requires not only accurate pitch detection but also assigning each note to a specific string-fret position, as the same pitch can be played at multiple fretboard positions. Existing approaches treat this as a standard classification problem, ignoring the musical and physical constraints that govern playable fingering sequences. We propose Noise2Fret, a diffusion model for audio-to-tablature transcription that generates tablature through a continuous latent representation of discrete fret and string targets, conditioned on spectral and audio features. To bridge the gap between pitch accuracy and physical playability, we introduce five auxiliary losses encoding Pitch-Class Distance, Positional Distance, Circle-of-Fifths Distance, String Similarity, and Hand-Span Feasibility directly into the training objective. Experiments on GuitarSet and GOAT datasets demonstrate that the model outperforms baselines while remaining computationally more efficient, and that the auxiliary losses yield consistent gains over the standard training objective.
CommentsAccepted for ISMIR 2026
