  Generative AI models have recently blossomed, significantly impacting
artistic and musical traditions. Research investigating how humans interact
with and deem these models is therefore crucial. Through a listening and
reflection study, we explore participants' perspectives on AI- vs
human-generated progressive metal, in symbolic format, using rock music as a
control group. AI-generated examples were produced by ProgGP, a
Transformer-based model. We propose a mixed methods approach to assess the
effects of generation type (human vs. AI), genre (progressive metal vs. rock),
and curation process (random vs. cherry-picked). This combines quantitative
feedback on genre congruence, preference, creativity, consistency, playability,
humanness, and repeatability, and qualitative feedback to provide insights into
listeners' experiences. A total of 32 progressive metal fans completed the
study. Our findings validate the use of fine-tuning to achieve genre-specific
specialization in AI music generation, as listeners could distinguish between
AI-generated rock and progressive metal. Despite some AI-generated excerpts
receiving similar ratings to human music, listeners exhibited a preference for
human compositions. Thematic analysis identified key features for genre and AI
vs. human distinctions. Finally, we consider the ethical implications of our
work in promoting musical data diversity within MIR research by focusing on an
under-explored genre.
