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：吉他指法谱是一种广泛用于吉他演奏者的音乐记谱形式，它不仅记录了乐曲的音乐内容，还包含了乐器的演奏手法与修饰细节。吉他指法谱转录是一项具有重要意义的任务，在音乐教育与娱乐领域有着广泛应用。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吉他指法谱转录模型易出现过拟合，且难以在不同数据集间泛化。为解决此问题，我们开发了一种合成大规模吉他指法谱转录数据集SynthTab的方法，采用了多种商用原声与电吉他插件。该数据集基于DadaGP中的指法谱构建，后者提供了丰富的曲库和所需的细节标注。我们提出的合成流程能够生成忠实遵循原始指法、演奏风格及技巧的音频，并具有多样化的音色。实验表明，在SynthTab上预训练最先进的吉他指法谱转录模型可提升同数据集测试中的转录准确性。更重要的是，该方法显著缓解了吉他指法谱转录模型在跨数据集评估中的过拟合问题。