  Deep generative models allow even novice composers to generate various
melodies by sampling latent vectors. However, finding the desired melody is
challenging since the latent space is unintuitive and high-dimensional. In this
work, we present an interactive system that supports generative melody
composition with human-in-the-loop Bayesian optimization (BO). This system
takes a mixed-initiative approach; the system generates candidate melodies to
evaluate, and the user evaluates them and provides preferential feedback (i.e.,
picking the best melody among the candidates) to the system. This process is
iteratively performed based on BO techniques until the user finds the desired
melody. We conducted a pilot study using our prototype system, suggesting the
potential of this approach.
