  One of the decisions that arise when designing a neural network for any
application is how the data should be represented in order to be presented to,
and possibly generated by, a neural network. For audio, the choice is less
obvious than it seems to be for visual images, and a variety of representations
have been used for different applications including the raw digitized sample
stream, hand-crafted features, machine discovered features, MFCCs and variants
that include deltas, and a variety of spectral representations. This paper
reviews some of these representations and issues that arise, focusing
particularly on spectrograms for generating audio using neural networks for
style transfer.
