Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
This addresses the need for automated, playable guitar transcriptions in Music Information Retrieval, though it appears incremental as it builds on existing transformer architectures.
The paper tackled the problem of transcribing MIDI sequences into guitar tablature, which lacks playability information, by introducing the Fretting-Transformer model, and it demonstrated superior performance over baseline methods like A* and commercial applications such as Guitar Pro.
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.