ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production
This work addresses symbolic music generation for progressive metal production, offering a tool for musicians and producers, but it is incremental as it builds on existing tokenization and model approaches.
The researchers tackled generating progressive metal music by fine-tuning a Transformer model on a dataset of 173 songs, enabling it to create multiple instrumental parts, and demonstrated its utility by producing a fully mixed song with human collaboration.
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.