  Texture synthesis techniques based on matching the Gram matrix of feature
activations in neural networks have achieved spectacular success in the image
domain. In this paper we extend these techniques to the audio domain. We
demonstrate that synthesizing diverse audio textures is challenging, and argue
that this is because audio data is relatively low-dimensional. We therefore
introduce two new terms to the original Grammian loss: an autocorrelation term
that preserves rhythm, and a diversity term that encourages the optimization
procedure to synthesize unique textures. We quantitatively study the impact of
our design choices on the quality of the synthesized audio by introducing an
audio analogue to the Inception loss which we term the VGGish loss. We show
that there is a trade-off between the diversity and quality of the synthesized
audio using this technique. We additionally perform a number of experiments to
qualitatively study how these design choices impact the quality of the
synthesized audio. Finally we describe the implications of these results for
the problem of audio style transfer.
