# towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model

arXiv: https://arxiv.org/abs/2202.09907 · HTML: https://mlsanity.com/paper/2202.09907/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2022.

## Built on by (most cited first, 9 of 9)

- [Automatic Piano Transcription with Hierarchical Frequency-Time Transformer](https://mlsanity.com/paper/2307.04305/) (2023)
- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription](https://mlsanity.com/paper/2212.03023/) (2022)
- [Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches](https://mlsanity.com/paper/2504.04751/) (2025)
- [Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models](https://mlsanity.com/paper/2411.14972/) (2024)
- [Velocity Prediction in Automatic Guitar Transcription](https://mlsanity.com/paper/2606.24912/) (2026)
- [Music Restoration via Latent Operator Optimization and Diffusion Model Priors](https://mlsanity.com/paper/2608.01972/) (2026)
- [Playability-Aware Audio-to-Tablature Guitar Transcription via Diffusion Models](https://mlsanity.com/paper/2608.30854/) (2026)
- [TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription](https://mlsanity.com/paper/2609.11904/) (2026)

## Abstract (arXiv)

In this paper, we propose a new dataset named EGDB, that con-tains transcriptions of the electric guitar performance of 240 tab-latures rendered with different tones. Moreover, we benchmark theperformance of two well-known transcription models proposed orig-inally for the piano on this dataset, along with a multi-loss Trans-former model that we newly propose. Our evaluation on this datasetand a separate set of real-world recordings demonstrate the influenceof timbre on the accuracy of guitar sheet transcription, the potentialof using multiple losses for Transformers, as well as the room forfurther improvement for this task.