# Leveraging Real Electric Guitar Tones and Effects to Improve Robustness in Guitar Tablature Transcription Modeling

arXiv: https://arxiv.org/abs/2405.14679 · HTML: https://mlsanity.com/paper/2405.14679/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2024.

## Built on by (most cited first, 3 of 3)

- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [Clean2FX: Label-conditioned modeling for clean-to-effect guitar audio transformations](https://mlsanity.com/paper/2607.08863/) (2026)
- [Synthesis and editing of multi-instrument audio mixtures using scalar-quantised latents with MIDI Span conditioning](https://mlsanity.com/paper/2609.25546/) (2026)

## Abstract (arXiv)

Guitar tablature transcription (GTT) aims at automatically generating symbolic representations from real solo guitar performances. Due to its applications in education and musicology, GTT has gained traction in recent years. However, GTT robustness has been limited due to the small size of available datasets. Researchers have recently used synthetic data that simulates guitar performances using pre-recorded or computer-generated tones and can be automatically generated at large scales. The present study complements these efforts by demonstrating that GTT robustness can be improved by including synthetic training data created using recordings of real guitar tones played with different audio effects. We evaluate our approach on a new evaluation dataset with professional solo guitar performances that we composed and collected, featuring a wide array of tones, chords, and scales.