  Recent years have seen increasing interest in applying deep learning methods
to the modeling of guitar amplifiers or effect pedals. Existing methods are
mainly based on the supervised approach, requiring temporally-aligned data
pairs of unprocessed and rendered audio. However, this approach does not scale
well, due to the complicated process involved in creating the data pairs. A
very recent work done by Wright et al. has explored the potential of leveraging
unpaired data for training, using a generative adversarial network (GAN)-based
framework. This paper extends their work by using more advanced discriminators
in the GAN, and using more unpaired data for training. Specifically, drawing
inspiration from recent advancements in neural vocoders, we employ in our
GAN-based model for guitar amplifier modeling two sets of discriminators, one
based on multi-scale discriminator (MSD) and the other multi-period
discriminator (MPD). Moreover, we experiment with adding unprocessed audio
signals that do not have the corresponding rendered audio of a target tone to
the training data, to see how much the GAN model benefits from the unpaired
data. Our experiments show that the proposed two extensions contribute to the
modeling of both low-gain and high-gain guitar amplifiers.
