  Music Inpainting is the task of filling in missing or lost information in a
piece of music. We investigate this task from an interactive music creation
perspective. To this end, a novel deep learning-based approach for musical
score inpainting is proposed. The designed model takes both past and future
musical context into account and is capable of suggesting ways to connect them
in a musically meaningful manner. To achieve this, we leverage the
representational power of the latent space of a Variational Auto-Encoder and
train a Recurrent Neural Network which learns to traverse this latent space
conditioned on the past and future musical contexts. Consequently, the designed
model is capable of generating several measures of music to connect two musical
excerpts. The capabilities and performance of the model are showcased by
comparison with competitive baselines using several objective and subjective
evaluation methods. The results show that the model generates meaningful
inpaintings and can be used in interactive music creation applications.
Overall, the method demonstrates the merit of learning complex trajectories in
the latent spaces of deep generative models.
