将MIDI自动转为吉他六线谱，解决指法模糊与可演奏性问题。
Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
- 基于T5架构的编码器-解码器模型，把转谱当符号翻译任务处理。
- 在多个数据集上优于A*算法和Guitar Pro等商业工具。
- 支持调弦和变调夹设置，提升实际演奏可行性，适合吉他学习者。
音乐转录在音乐信息检索中至关重要，尤其对吉他等弦乐器而言，标准符号记谱如MIDI缺少关键指法可演奏信息。本文提出Fretting-Transformer，一种基于T5 Transformer架构的编码器-解码器模型，用于将MIDI序列自动转为吉他六线谱。通过将任务建模为符号翻译问题，该模型有效应对弦位模糊和物理可演奏性等挑战。研究使用DadaGP、GuitarToday和Leduc等多个数据集，并设计了新颖的数据预处理与分词策略。我们提出了六线谱准确率与可演奏性评估指标，进行定量分析。实验表明，Fretting-Transformer在性能上超过基线方法A*及商业软件Guitar Pro。结合上下文敏感处理与调弦/变调夹条件输入，显著提升表现，为自动化吉他转谱提供坚实基础。
原文摘要 · Abstract (English)
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.
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