  Drawing an analogy with automatic image completion systems, we propose Music
SketchNet, a neural network framework that allows users to specify partial
musical ideas guiding automatic music generation. We focus on generating the
missing measures in incomplete monophonic musical pieces, conditioned on
surrounding context, and optionally guided by user-specified pitch and rhythm
snippets. First, we introduce SketchVAE, a novel variational autoencoder that
explicitly factorizes rhythm and pitch contour to form the basis of our
proposed model. Then we introduce two discriminative architectures,
SketchInpainter and SketchConnector, that in conjunction perform the guided
music completion, filling in representations for the missing measures
conditioned on surrounding context and user-specified snippets. We evaluate
SketchNet on a standard dataset of Irish folk music and compare with models
from recent works. When used for music completion, our approach outperforms the
state-of-the-art both in terms of objective metrics and subjective listening
tests. Finally, we demonstrate that our model can successfully incorporate
user-specified snippets during the generation process.
