  In recent years generative adversarial networks (GANs) have been used to
supplement datasets within the field of marine bioacoustics. This is driven by
factors such as the cost to collect data, data sparsity and aid preprocessing.
One notable challenge with marine bioacoustic data is the low signal-to-noise
ratio (SNR) posing difficulty when applying deep learning techniques such as
GANs. This work investigates the effect SNR has on the audio-based GAN
performance and examines three different evaluation methodologies for GAN
performance, yielding interesting results on the effects of SNR on GANs,
specifically WaveGAN.
