Abstract
Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription.
Abstract (translated)
音乐转录在音乐信息检索（MIR）中扮演着至关重要的角色，尤其是在像吉他这样的弦乐器领域，因为符号化的乐谱如MIDI缺乏关键的演奏性信息。本文介绍了一种名为Fretting-Transformer的编码器-解码器模型，该模型利用了T5变压器架构来自动将MIDI序列转换为吉他的六线谱（tablature）。通过将任务视为一种符号翻译问题，该模型解决了包括琴弦品格模糊性和物理演奏性在内的关键挑战。所提出的系统使用多样化的数据集，如DadaGP、GuitarToday和Leduc，并采用了创新的数据预处理和标记策略。我们开发了用于评估六线谱准确性和演奏性的度量标准，以便定量地评价模型的性能。实验结果表明，Fretting-Transformer在基线方法（如A*算法）和商业应用（如Guitar Pro）上都表现出色。通过集成上下文敏感处理和调音/卡普奥调节，进一步增强了该模型的性能，为自动吉他转录未来的发展奠定了坚实的基础。
URL
https://arxiv.org/abs/2506.14223
