  There has been fascinating work on creating artistic transformations of
images by Gatys. This was revolutionary in how we can in some sense alter the
'style' of an image while generally preserving its 'content'. In our work, we
present a method for creating new sounds using a similar approach, treating it
as a style-transfer problem, starting from a random-noise input signal and
iteratively using back-propagation to optimize the sound to conform to
filter-outputs from a pre-trained neural architecture of interest.
  For demonstration, we investigate two different tasks, resulting in bandwidth
expansion/compression, and timbral transfer from singing voice to musical
instruments. A feature of our method is that a single architecture can generate
these different audio-style-transfer types using the same set of parameters
which otherwise require different complex hand-tuned diverse signal processing
pipelines.
