  Two modest-sized symbolic corpora of post-tonal and post-metric keyboard
music have been constructed, one algorithmic, the other improvised. Deep
learning models of each have been trained and largely optimised. Our purpose is
to obtain a model with sufficient generalisation capacity that in response to a
small quantity of separate fresh input seed material, it can generate outputs
that are distinctive, rather than recreative of the learned corpora or the seed
material. This objective has been first assessed statistically, and as judged
by k-sample Anderson-Darling and Cramer tests, has been achieved. Music has
been generated using the approach, and informal judgements place it roughly on
a par with algorithmic and composed music in related forms. Future work will
aim to enhance the model such that it can be evaluated in relation to
expression, meaning and utility in real-time performance.
