  In this paper, we investigate the potential effect of the adversarially
training on the robustness of six advanced deep neural networks against a
variety of targeted and non-targeted adversarial attacks. We firstly show that,
the ResNet-56 model trained on the 2D representation of the discrete wavelet
transform appended with the tonnetz chromagram outperforms other models in
terms of recognition accuracy. Then we demonstrate the positive impact of
adversarially training on this model as well as other deep architectures
against six types of attack algorithms (white and black-box) with the cost of
the reduced recognition accuracy and limited adversarial perturbation. We run
our experiments on two benchmarking environmental sound datasets and show that
without any imposed limitations on the budget allocations for the adversary,
the fooling rate of the adversarially trained models can exceed 90\%. In other
words, adversarial attacks exist in any scales, but they might require higher
adversarial perturbations compared to non-adversarially trained models.
