PreGLAM-MMM: Application and evaluation of affective adaptive generative music in video games

Cale Plut, Philippe Pasquier, Jeff Ens, Renaud Bougueng Tchemeube

Abstract: We present and evaluate an application of affective adaptive generative music in a single-player, action-RPG video game. We create a score that serves as an audience to the gameplay, based on the output of PreGLAM, which models the emotional perception of a game audience. We use the Multi-track Music Machine to expand and extend a composed adaptive musical score, and we use industry-standard production techniques to synthesize and perform all of our musical scores. We evaluate our application of generative music in comparison to two composed scores, one adaptive and one linear. Our generative score is rated as nearly equivalent to a composed linear score in perceptions of emotional congruency, immersion, and preference.