  Deep generative modeling has the potential to cause significant harm to
society. Recognizing this threat, a magnitude of research into detecting
so-called "Deepfakes" has emerged. This research most often focuses on the
image domain, while studies exploring generated audio signals have, so-far,
been neglected. In this paper we make three key contributions to narrow this
gap. First, we provide researchers with an introduction to common signal
processing techniques used for analyzing audio signals. Second, we present a
novel data set, for which we collected nine sample sets from five different
network architectures, spanning two languages. Finally, we supply practitioners
with two baseline models, adopted from the signal processing community, to
facilitate further research in this area.
