ShredGP: Guitarist Style-Conditioned Tablature Generation

TL;DR

ShredGP is a Transformer-based model that generates guitar tablatures conditioned on specific guitarists' styles, validated through statistical analysis and a style classifier, advancing AI-assisted guitar music creation.

Contribution

Introduces ShredGP, a novel style-conditioned guitar tablature generator using Transformer architecture and a computational musicology approach to analyze and replicate guitarist styles.

Findings

ShredGP can generate guitar tabs matching specific guitarist styles.

Statistical analysis confirms significant stylistic differences between guitarists.

A BERT-based classifier effectively evaluates style congruence of generated tabs.

Abstract

GuitarPro format tablatures are a type of digital music notation that encapsulates information about guitar playing techniques and fingerings. We introduce ShredGP, a GuitarPro tablature generative Transformer-based model conditioned to imitate the style of four distinct iconic electric guitarists. In order to assess the idiosyncrasies of each guitar player, we adopt a computational musicology methodology by analysing features computed from the tokens yielded by the DadaGP encoding scheme. Statistical analyses of the features evidence significant differences between the four guitarists. We trained two variants of the ShredGP model, one using a multi-instrument corpus, the other using solo guitar data. We present a BERT-based model for guitar player classification and use it to evaluate the generated examples. Overall, results from the classifier show that ShredGP is able to generate content congruent with the style of the targeted guitar player. Finally, we reflect on prospective applications for ShredGP for human-AI music interaction.