  Spurred by the potential of deep learning, computational music generation has
gained renewed academic interest. A crucial issue in music generation is that
of user control, especially in scenarios where the music generation process is
conditioned on existing musical material. Here we propose a model for
conditional kick drum track generation that takes existing musical material as
input, in addition to a low-dimensional code that encodes the desired relation
between the existing material and the new material to be generated. These
relational codes are learned in an unsupervised manner from a music dataset. We
show that codes can be sampled to create a variety of musically plausible kick
drum tracks and that the model can be used to transfer kick drum patterns from
one song to another. Lastly, we demonstrate that the learned codes are largely
invariant to tempo and time-shift.
