  The research in Environmental Sound Classification (ESC) has been
progressively growing with the emergence of deep learning algorithms. However,
data scarcity poses a major hurdle for any huge advance in this domain. Data
augmentation offers an excellent solution to this problem. While Generative
Adversarial Networks (GANs) have been successful in generating synthetic speech
and sounds of musical instruments, they have hardly been applied to the
generation of environmental sounds. This paper presents EnvGAN, the first ever
application of GANs for the adversarial generation of environmental sounds. Our
experiments on three standard ESC datasets illustrate that the EnvGAN can
synthesize audio similar to the ones in the datasets. The suggested method of
augmentation outshines most of the futuristic techniques for audio
augmentation.
