Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription

TL;DR

The paper introduces the Fretting-Transformer, an encoder-decoder model based on T5 transformer architecture, for automated MIDI to guitar tablature transcription, addressing challenges like string-fret ambiguity and improving accuracy over baselines.

Contribution

It presents a novel transformer-based model with specialized data processing and evaluation metrics for accurate and playable MIDI to guitar tab transcription.

Findings

Fretting-Transformer outperforms baseline methods in accuracy.

The model effectively handles string-fret ambiguity.

Enhanced performance with context-sensitive processing and tuning/capo conditioning.

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.