Guitar tablatures enrich the structure of traditional music notation by assigning each note to a string and fret of a guitar in a particular tuning, indicating precisely where to play the note on the instrument. The problem of generating tablature from a symbolic music representation involves inferring this string and fret assignment per note across an entire composition or performance. On the guitar, multiple string-fret assignments are possible for most pitches, which leads to a large combinatorial space that prevents exhaustive search approaches. Most modern methods use constraint-based dynamic programming to minimize some cost function (e.g.\ hand position movement). In this work, we introduce a novel deep learning solution to symbolic guitar tablature estimation. We train an encoder-decoder Transformer model in a masked language modeling paradigm to assign notes to strings. The model is first pre-trained on DadaGP, a dataset of over 25K tablatures, and then fine-tuned on a curated set of professionally transcribed guitar performances. Given the subjective nature of assessing tablature quality, we conduct a user study amongst guitarists, wherein we ask participants to rate the playability of multiple versions of tablature for the same four-bar excerpt. The results indicate our system significantly outperforms competing algorithms.

翻译：吉他六线谱通过将每个音符分配到特定调弦下吉他的特定琴弦和品格，明确指示音符在乐器上的演奏位置，从而丰富了传统音乐记谱法的结构。从符号化音乐表示生成六线谱的问题，涉及在整个乐曲或演奏中为每个音符推断其对应的琴弦与品格分配。在吉他上，大多数音高存在多种可行的弦-品分配方案，这导致了一个巨大的组合空间，使得穷举搜索方法不可行。现有主流方法采用基于约束的动态规划来最小化某些成本函数（例如手部位置移动量）。本研究提出了一种新颖的符号化吉他六线谱估计深度学习解决方案。我们在掩码语言建模范式下训练了一个编码器-解码器Transformer模型，用于将音符分配到琴弦。该模型首先在包含超过2.5万首六线谱的DadaGP数据集上进行预训练，随后在精心筛选的专业转录吉他演奏数据集上进行微调。鉴于评估六线谱质量的主观性，我们在吉他手中开展了用户研究，要求参与者对同一四小节片段的不同六线谱版本进行可演奏性评分。结果表明，我们的系统显著优于现有竞争算法。