  This early example of neural synthesis is a proof-of-concept for how machine
learning can drive new types of music software. Creating music can be as simple
as specifying a set of music influences on which a model trains. We demonstrate
a method for generating albums that imitate bands in experimental music genres
previously unrealized by traditional synthesis techniques (e.g. additive,
subtractive, FM, granular, concatenative). Raw audio is generated
autoregressively in the time-domain using an unconditional SampleRNN. We create
six albums this way. Artwork and song titles are also generated using materials
from the original artists' back catalog as training data. We try a
fully-automated method and a human-curated method. We discuss its potential for
machine-assisted production.
