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.

翻译：近期符号音乐生成领域的研究表明，采用基于GuitarPro格式的令牌化方法作为输入与输出表征具有重要价值——该符号格式支持吉他演奏属性的表达。本研究通过微调预训练Transformer模型加以拓展，在自建包含173首前卫金属歌曲的ProgGP数据集上进行训练，旨在通过人机协作模式生成该风格的创作作品。该模型能够生成多声部包含吉他、贝斯、爵士鼓、钢琴及管弦乐部分。我们采用混合研究方法，结合计算音乐学范式的定量分析与基于实践研究范式的定性分析，对生成音乐的有效性进行验证。最后，通过将该模型作为创作工具，由人类金属制作人基于AI生成音乐完成完整制作与混音的一首前卫金属歌曲，证明了该模型的实用价值。