# GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures

arXiv: https://arxiv.org/abs/2509.22655 · HTML: https://mlsanity.com/paper/2509.22655/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [RAVE: A variational autoencoder for fast and high-quality neural audio synthesis](https://mlsanity.com/paper/2111.05011/) (2021)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models](https://mlsanity.com/paper/2107.14653/) (2021)
- [A Data-Driven Analysis of Robust Automatic Piano Transcription](https://mlsanity.com/paper/2402.01424/) (2024)
- [High Resolution Guitar Transcription via Domain Adaptation](https://mlsanity.com/paper/2402.15258/) (2024)
- [GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers](https://mlsanity.com/paper/2302.05393/) (2023)
- [towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model](https://mlsanity.com/paper/2202.09907/) (2022)
- [GAPS: A Large and Diverse Classical Guitar Dataset and Benchmark Transcription Model](https://mlsanity.com/paper/2408.08653/) (2024)
- [ShredGP: Guitarist Style-Conditioned Tablature Generation](https://mlsanity.com/paper/2307.05324/) (2023)
- [General Purpose Audio Effect Removal](https://mlsanity.com/paper/2308.16177/) (2023)
- [Adversarial Guitar Amplifier Modelling With Unpaired Data](https://mlsanity.com/paper/2211.00943/) (2022)
- [FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription](https://mlsanity.com/paper/2212.03023/) (2022)
- [Performance Conditioning for Diffusion-Based Multi-Instrument Music Synthesis](https://mlsanity.com/paper/2309.12283/) (2023)
- [Expressive Acoustic Guitar Sound Synthesis with an Instrument-Specific Input Representation and Diffusion Outpainting](https://mlsanity.com/paper/2401.13498/) (2024)
- [Distortion Audio Effects: Learning How to Recover the Clean Signal](https://mlsanity.com/paper/2202.01664/) (2022)
- [ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production](https://mlsanity.com/paper/2307.05328/) (2023)
- [Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control](https://mlsanity.com/paper/2407.10646/) (2024)
- [MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling](https://mlsanity.com/paper/2408.05024/) (2024)
- [Guitar-TECHS: An Electric Guitar Dataset Covering Techniques, Musical Excerpts, Chords and Scales Using a Diverse Array of Hardware](https://mlsanity.com/paper/2501.03720/) (2025)
- [Leveraging Real Electric Guitar Tones and Effects to Improve Robustness in Guitar Tablature Transcription Modeling](https://mlsanity.com/paper/2405.14679/) (2024)
- [Modeling Bends in Popular Music Guitar Tablatures](https://mlsanity.com/paper/2308.12307/) (2023)

## Built on by (most cited first, 6 of 6)

- [GuitarFlow: Realistic Electric Guitar Synthesis From Tablatures via Flow Matching and Style Transfer](https://mlsanity.com/paper/2510.21872/) (2025)
- [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)
- [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)

In recent years, the guitar has received increased attention from the music information retrieval (MIR) community driven by the challenges posed by its diverse playing techniques and sonic characteristics. Mainly fueled by deep learning approaches, progress has been limited by the scarcity and limited annotations of datasets. To address this, we present the Guitar On Audio and Tablatures (GOAT) dataset, comprising 5.9 hours of unique high-quality direct input audio recordings of electric guitars from a variety of different guitars and players. We also present an effective data augmentation strategy using guitar amplifiers which delivers near-unlimited tonal variety, of which we provide a starting 29.5 hours of audio. Each recording is annotated using guitar tablatures, a guitar-specific symbolic format supporting string and fret numbers, as well as numerous playing techniques. For this we utilise both the Guitar Pro format, a software for tablature playback and editing, and a text-like token encoding. Furthermore, we present competitive results using GOAT for MIDI transcription and preliminary results for a novel approach to automatic guitar tablature transcription. We hope that GOAT opens up the possibilities to train novel models on a wide variety of guitar-related MIR tasks, from synthesis to transcription to playing technique detection.