  Within Music Information Retrieval (MIR), prominent tasks -- including
pitch-tracking, source-separation, super-resolution, and synthesis -- typically
call for specialised methods, despite their similarities. Conditional
Generative Adversarial Networks (cGANs) have been shown to be highly versatile
in learning general image-to-image translations, but have not yet been adapted
across MIR. In this work, we present an end-to-end supervisable architecture to
perform all aforementioned audio tasks, consisting of a WaveNet synthesiser
conditioned on the output of a jointly-trained cGAN spectrogram translator. In
doing so, we demonstrate the potential of such flexible techniques to unify MIR
tasks, promote efficient transfer learning, and converge research to the
improvement of powerful, general methods. Finally, to the best of our
knowledge, we present the first application of GANs to guided instrument
synthesis.
