Computer Assisted Composition with Recurrent Neural Networks
  Sequence modeling with neural networks has lead to powerful models of
symbolic music data. We address the problem of exploiting these models to reach
creative musical goals, by combining with human input. To this end we
generalise previous work, which sampled Markovian sequence models under the
constraint that the sequence belong to the language of a given finite state
machine provided by the human. We consider more expressive non-Markov models,
thereby requiring approximate sampling which we provide in the form of an
efficient sequential Monte Carlo method. In addition we provide and compare
with a beam search strategy for conditional probability maximisation.
  Our algorithms are capable of convincingly re-harmonising famous musical
works. To demonstrate this we provide visualisations, quantitative experiments,
a human listening test and audio examples. We find both the sampling and
optimisation procedures to be effective, yet complementary in character. For
the case of highly permissive constraint sets, we find that sampling is to be
preferred due to the overly regular nature of the optimisation based results.
The generality of our algorithms permits countless other creative applications.
