  In this paper, we compare different audio signal representations, including
the raw audio waveform and a variety of time-frequency representations, for the
task of audio synthesis with Generative Adversarial Networks (GANs). We conduct
the experiments on a subset of the NSynth dataset. The architecture follows the
benchmark Progressive Growing Wasserstein GAN. We perform experiments both in a
fully non-conditional manner as well as conditioning the network on the pitch
information. We quantitatively evaluate the generated material utilizing
standard metrics for assessing generative models, and compare training and
sampling times. We show that complex-valued as well as the magnitude and
Instantaneous Frequency of the Short-Time Fourier Transform achieve the best
results, and yield fast generation and inversion times. The code for feature
extraction, training and evaluating the model is available online.
