  Generative Adversarial Networks (GANs) have achieved excellent audio
synthesis quality in the last years. However, making them operable with
semantically meaningful controls remains an open challenge. An obvious approach
is to control the GAN by conditioning it on metadata contained in audio
datasets. Unfortunately, audio datasets often lack the desired annotations,
especially in the musical domain. A way to circumvent this lack of annotations
is to generate them, for example, with an automatic audio-tagging system. The
output probabilities of such systems (so-called "soft labels") carry rich
information about the characteristics of the respective audios and can be used
to distill the knowledge from a teacher model into a student model. In this
work, we perform knowledge distillation from a large audio tagging system into
an adversarial audio synthesizer that we call DarkGAN. Results show that
DarkGAN can synthesize musical audio with acceptable quality and exhibits
moderate attribute control even with out-of-distribution input conditioning. We
release the code and provide audio examples on the accompanying website.
