  We investigate applying audio manipulations using pretrained neural
network-based autoencoders as an alternative to traditional signal processing
methods, since the former may provide greater semantic or perceptual
organization. To establish the potential of this approach, we first establish
if representations from these models encode information about manipulations. We
carry out experiments and produce visualizations using representations from two
different pretrained autoencoders. Our findings indicate that, while some
information about audio manipulations is encoded, this information is both
limited and encoded in a non-trivial way. This is supported by our attempts to
visualize these representations, which demonstrated that trajectories of
representations for common manipulations are typically nonlinear and content
dependent, even for linear signal manipulations. As a result, it is not yet
clear how these pretrained autoencoders can be used to manipulate audio
signals, however, our results indicate this may be due to the lack of
disentanglement with respect to common audio manipulations.
