  Today's generative neural networks allow the creation of high-quality
synthetic speech at scale. While we welcome the creative use of this new
technology, we must also recognize the risks. As synthetic speech is abused for
monetary and identity theft, we require a broad set of deepfake identification
tools. Furthermore, previous work reported a limited ability of deep
classifiers to generalize to unseen audio generators. We study the frequency
domain fingerprints of current audio generators. Building on top of the
discovered frequency footprints, we train excellent lightweight detectors that
generalize. We report improved results on the WaveFake dataset and an extended
version. To account for the rapid progress in the field, we extend the WaveFake
dataset by additionally considering samples drawn from the novel Avocodo and
BigVGAN networks. For illustration purposes, the supplementary material
contains audio samples of generator artifacts.
