[1I5-GS-4b] Web intelligence

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[1I5-GS-4b-03] A Global Expansion Strategy for Japanese Music Using AI-Generated Music The Engagement Effect of a Bold Genre Shift from J-POP to Metal

〇TONOOKA YASUNORI¹, NISHIMURA TAKUICHI¹ (1. JAIST)

Keywords:

AI music generation,genre transfer,cultural hybrid,data-driven analysis,traditional music

AI-driven music generation tools such as Suno and Udio have made large-scale genre transfer increasingly accessible, opening new pathways for the global expansion of Japanese popular music. This study analyzes 1,203 YouTube remix videos to quantitatively examine how genre distance shapes viewer engagement, with particular attention to J-POP-to-metal transformations. Results show that bold genre conversions (distance 4) yielded the highest overall engagement, while J-POP-to-metal remixes achieved a median of 67,159 views — 6.2 times higher than the distance-4 average — driven by musical affinity, nostalgia, and global metal fandom. Within this pair, moderate transformations (distance 2–3) outperformed more extreme ones, reflecting J-POP's inherent rock elements. Genre distance correlated strongly with acoustic distance (r = 0.969, p < 0.001), validating the metric. Directional asymmetry analysis (DAI = 0.70) further suggests that transfer direction is an independent factor in engagement, warranting future investigation with larger reverse-direction samples.