Guitar tablature transcription (GTT) aims at automatically generating symbolic representations from real solo guitar performances. Due to its applications in education and musicology, GTT has gained traction in recent years. However, GTT robustness has been limited due to the small size of available datasets. Researchers have recently used synthetic data that simulates guitar performances using pre-recorded or computer-generated tones and can be automatically generated at large scales. The present study complements these efforts by demonstrating that GTT robustness can be improved by including synthetic training data created using recordings of real guitar tones played with different audio effects. We evaluate our approach on a new evaluation dataset with professional solo guitar performances that we composed and collected, featuring a wide array of tones, chords, and scales.

翻译：吉他六线谱转录旨在从真实独奏吉他演奏中自动生成符号化表示。因其在教育学与音乐学领域的应用价值，该技术近年来备受关注。然而，现有数据集规模较小限制了吉他六线谱转录的鲁棒性。近期研究者开始采用合成数据——通过预录制或计算机生成的音色模拟吉他演奏，这类数据可大规模自动生成。本研究通过证明使用真实吉他音色配合不同音频效果器录制的合成训练数据能够提升吉他六线谱转录的鲁棒性，对现有研究形成了有效补充。我们在自主创作并收集的专业独奏吉他演奏评估数据集上验证了该方法，该数据集涵盖了丰富的音色类型、和弦结构与音阶形态。