# ShredGP: Guitarist Style-Conditioned Tablature Generation

arXiv: https://arxiv.org/abs/2307.05324 · HTML: https://mlsanity.com/paper/2307.05324/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2023.

## Built on by (most cited first, 7 of 7)

- [The GigaMIDI Dataset with Features for Expressive Music Performance Detection](https://mlsanity.com/paper/2502.17726/) (2025)
- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling](https://mlsanity.com/paper/2408.05024/) (2024)
- [Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music](https://mlsanity.com/paper/2407.21615/) (2024)
- [Adaptable Symbolic Music Infilling with MIDI-RWKV](https://mlsanity.com/paper/2506.13001/) (2025)
- [From MIDI to Rich Tablatures: an Automatic Generative System incorporating Lead Guitarists' Fingering and Stylistic choices](https://mlsanity.com/paper/2407.09052/) (2024)
- [Guitar Chord Diagram Suggestion for Western Popular Music](https://mlsanity.com/paper/2407.14260/) (2024)

## Abstract (arXiv)

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.