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.

翻译：音乐转录在音乐信息检索中扮演着关键角色，尤其对于吉他等弦乐器而言，像MIDI这样的符号音乐记谱法缺乏关键的演奏性信息。本文提出了Fretting-Transformer，一种利用T5 Transformer架构的编码器-解码器模型，用于将MIDI序列自动转录为吉他指法谱。通过将该任务构建为符号翻译问题，该模型解决了包括弦-品模糊性和物理可演奏性在内的关键挑战。所提出的系统利用了多样化的数据集，包括DadaGP、GuitarToday和Leduc，并采用了新颖的数据预处理和标记化策略。我们开发了用于评估指法谱准确性和可演奏性的指标，以量化性能。实验结果表明，Fretting-Transformer超越了如A*等基线方法以及如Guitar Pro等商业应用。结合上下文敏感处理及调弦/变调夹条件化进一步提升了模型的性能，为未来自动化吉他转录的发展奠定了坚实基础。