  While generative adversarial networks (GANs) have been widely used in
research on audio generation, the training of a GAN model is known to be
unstable, time consuming, and data inefficient. Among the attempts to
ameliorate the training process of GANs, the idea of Projected GAN emerges as
an effective solution for GAN-based image generation, establishing the
state-of-the-art in different image applications. The core idea is to use a
pre-trained classifier to constrain the feature space of the discriminator to
stabilize and improve GAN training. This paper investigates whether Projected
GAN can similarly improve audio generation, by evaluating the performance of a
StyleGAN2-based audio-domain loop generation model with and without using a
pre-trained feature space in the discriminator. Moreover, we compare the
performance of using a general versus domain-specific classifier as the
pre-trained audio classifier. With experiments on both drum loop and synth loop
generation, we show that a general audio classifier works better, and that with
Projected GAN our loop generation models can converge around 5 times faster
without performance degradation.
