Guitar tablature is a form of music notation widely used among guitarists. It captures not only the musical content of a piece, but also its implementation and ornamentation on the instrument. Guitar Tablature Transcription (GTT) is an important task with broad applications in music education and entertainment. Existing datasets are limited in size and scope, causing state-of-the-art GTT models trained on such datasets to suffer from overfitting and to fail in generalization across datasets. To address this issue, we developed a methodology for synthesizing SynthTab, a large-scale guitar tablature transcription dataset using multiple commercial acoustic and electric guitar plugins. This dataset is built on tablatures from DadaGP, which offers a vast collection and the degree of specificity we wish to transcribe. The proposed synthesis pipeline produces audio which faithfully adheres to the original fingerings, styles, and techniques specified in the tablature with diverse timbre. Experiments show that pre-training state-of-the-art GTT model on SynthTab improves transcription accuracy in same-dataset tests. More importantly, it significantly mitigates overfitting problems of GTT models in cross-dataset evaluation.

翻译：吉他指法谱是一种在吉他手中广泛使用的音乐记谱形式，它不仅记录乐曲的音乐内容，还捕捉其在乐器上的具体指法、演奏技法及装饰细节。吉他指法谱转录是一项重要任务，在音乐教育与娱乐领域具有广阔应用前景。现有数据集在规模和覆盖范围上存在局限性，导致基于这些数据集训练的最先进GTT模型出现过拟合问题，且无法在不同数据集间有效泛化。为解决这一问题，我们开发了一套方法论，利用多种商用原声吉他及电吉他插件合成了大规模吉他指法谱转录数据集SynthTab。该数据集基于DadaGP的指法谱构建，DadaGP提供了丰富的曲库及我们所需转录的详细程度。所提出的合成管道能够生成忠实遵循原始指法、风格及技法说明、且具有多样音色的音频。实验表明，在SynthTab上预训练最先进的GTT模型可提升同数据集测试中的转录精度。更关键的是，该方法能显著缓解GTT模型在跨数据集评估中的过拟合问题。