ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production
Jackson Loth, Pedro Sarmento, CJ Carr, Zack Zukowski, Mathieu, Barthet

TL;DR
This paper presents ProgGP, a Transformer-based model fine-tuned on a progressive metal dataset, capable of generating multi-instrument compositions in GuitarPro format to assist human music production.
Contribution
It introduces a novel application of tokenized GuitarPro data with a Transformer model for genre-specific music generation and demonstrates its practical use in producing a complete metal song.
Findings
Model generates diverse multi-instrument progressive metal parts.
Quantitative and qualitative analyses validate the musical quality.
AI-assisted production results in a fully mixed metal track.
Abstract
Recent work in the field of symbolic music generation has shown value in using a tokenization based on the GuitarPro format, a symbolic representation supporting guitar expressive attributes, as an input and output representation. We extend this work by fine-tuning a pre-trained Transformer model on ProgGP, a custom dataset of 173 progressive metal songs, for the purposes of creating compositions from that genre through a human-AI partnership. Our model is able to generate multiple guitar, bass guitar, drums, piano and orchestral parts. We examine the validity of the generated music using a mixed methods approach by combining quantitative analyses following a computational musicology paradigm and qualitative analyses following a practice-based research paradigm. Finally, we demonstrate the value of the model by using it as a tool to create a progressive metal song, fully produced and…
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Taxonomy
TopicsMusic Technology and Sound Studies · Music and Audio Processing
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Layer Normalization · Absolute Position Encodings · Adam · Dense Connections · Softmax · Position-Wise Feed-Forward Layer · Label Smoothing
