Adversarial audio attacks can be considered as a small perturbation\nunperceptive to human ears that is intentionally added to the audio signal and\ncauses a machine learning model to make mistakes. This poses a security concern\nabout the safety of machine learning models since the adversarial attacks can\nfool such models toward the wrong predictions. In this paper we first review\nsome strong adversarial attacks that may affect both audio signals and their 2D\nrepresentations and evaluate the resiliency of the most common machine learning\nmodel, namely deep learning models and support vector machines (SVM) trained on\n2D audio representations such as short time Fourier transform (STFT), discrete\nwavelet transform (DWT) and cross recurrent plot (CRP) against several\nstate-of-the-art adversarial attacks. Next, we propose a novel approach based\non pre-processed DWT representation of audio signals and SVM to secure audio\nsystems against adversarial attacks. The proposed architecture has several\npreprocessing modules for generating and enhancing spectrograms including\ndimension reduction and smoothing. We extract features from small patches of\nthe spectrograms using speeded up robust feature (SURF) algorithm which are\nfurther used to generate a codebook using the K-Means++ algorithm. Finally,\ncodewords are used to train a SVM on the codebook of the SURF-generated\nvectors. All these steps yield to a novel approach for audio classification\nthat provides a good trade-off between accuracy and resilience. Experimental\nresults on three environmental sound datasets show the competitive performance\nof proposed approach compared to the deep neural networks both in terms of\naccuracy and robustness against strong adversarial attacks.\n