  Audio textures are a subset of environmental sounds, often defined as having
stable statistical characteristics within an adequately large window of time
but may be unstructured locally. They include common everyday sounds such as
from rain, wind, and engines. Given that these complex sounds contain patterns
on multiple timescales, they are a challenge to model with traditional methods.
We introduce a novel modelling approach for textures, combining recurrent
neural networks trained at different levels of abstraction with a conditioning
strategy that allows for user-directed synthesis. We demonstrate the model's
performance on a variety of datasets, examine its performance on various
metrics, and discuss some potential applications.
