Synthesizing Engaging Music Using Dynamic Models of Statistical Surprisal

Synthesis of music content generally leverages the underlying statistical structure of music to develop generative models, able to create new musical expressions within the same genre. In this work, we explore the statistical structure of a musical corpus and its effect on modulating the attention of listeners. The study specifically explores listeners' engagement to newly synthesized music and tests the hypothesis that maximizing statistical surprisal would result in increased auditory salience. The study employs a dynamical statistical model to estimate melodic line surprisal and develops an optimization procedure using parametrized codebooks to synthesize musical segments that maximize statistical surprisal. A behavioral experiment with a dichotic listening task is designed to probe salience of the synthesized melodies against original melodies by measuring listeners' engagement in a continuous-fashion. Results indicate that we can control the salience of sounds by manipulating the statistical surprisal, guided by the complexity of the temporal structure of the musical corpus. This work suggests that future work in automated music synthesis could leverage statistical models of music beyond musical aesthetics to also manipulate the degree of engagement.