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 mixed by a human metal producer
based on AI-generated music.

本文利用基于 GuitarPro 格式的记号作为输入和输出表现的 Fine-Tuning 预训练的 Transformer 模型，结合混合方法的定量和定性分析，创作出了一首 Prog 金属乐曲，并展示了该模型的价值。