  Generative Adversarial Networks (GANs) currently achieve the state-of-the-art
sound synthesis quality for pitched musical instruments using a 2-channel
spectrogram representation consisting of log magnitude and instantaneous
frequency (the "IFSpectrogram"). Many other synthesis systems use
representations derived from the magnitude spectra, and then depend on a
backend component to invert the output magnitude spectrograms that generally
result in audible artefacts associated with the inversion process. However, for
signals that have closely-spaced frequency components such as non-pitched and
other noisy sounds, training the GAN on the 2-channel IFSpectrogram
representation offers no advantage over the magnitude spectra based
representations. In this paper, we propose that training GANs on single-channel
magnitude spectra, and using the Phase Gradient Heap Integration (PGHI)
inversion algorithm is a better comprehensive approach for audio synthesis
modeling of diverse signals that include pitched, non-pitched, and dynamically
complex sounds. We show that this method produces higher-quality output for
wideband and noisy sounds, such as pops and chirps, compared to using the
IFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable
to using the IFSpectrogram, even while using a simpler representation with half
the memory requirements.
