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.
DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
Queen Mary University of London（伦敦玛丽女王大学）；Academia Sinica（中央研究院）；Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Dadabots；Taiwan AI Labs（台湾人工智能实验室）
Leveraging Real Electric Guitar Tones and Effects to Improve Robustness in Guitar Tablature Transcription Modeling
National Autonomous University of Mexico（墨西哥国立自治大学）；Federal University of Rio de Janeiro（里约热内卢联邦大学）；Discovery Partners Institute（发现合作伙伴研究所）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；New York University（纽约大学）
