GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures
This addresses the scarcity of annotated data for guitar-related tasks in music information retrieval, though it is incremental as it builds on existing dataset efforts.
The authors tackled the problem of limited datasets for guitar music information retrieval by creating the GOAT dataset, which includes 5.9 hours of unique audio recordings with tablature annotations and an augmentation strategy providing 29.5 hours of audio, and they reported competitive results for MIDI transcription and preliminary results for automatic tablature transcription.
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.