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.

翻译：近年来，生成对抗网络（GANs）已被用于扩充海洋生物声学领域的数据集。这一应用主要受数据采集成本、数据稀疏性以及预处理需求等因素驱动。海洋生物声学数据的一个显著挑战是低信噪比（SNR），这给GANs等深度学习技术的应用带来了困难。本研究探讨了信噪比对基于音频的GAN性能的影响，并考察了三种不同的GAN性能评估方法，得出了关于SNR对GANs（特别是WaveGAN）影响的有趣结论。