Audio surveillance is gaining in the last years wide interest. This is due to the large number of situations in which this kind of systems can be used, either alone or combined with video-based algorithms. In this paper we propose a deep learning method to automatically recognize events of interest in the context of audio surveillance (namely screams, broken glasses and gun shots). The audio stream is represented by a gammatonegram image. We propose a 21-layer CNN to which we feed sections of the gammatonegram representation. At the output of this CNN there are units that correspond to the classes. We trained the CNN, called AReN, by taking advantage of a problemdriven data augmentation, which extends the training dataset with gammatonegram images extracted by sounds acquired with different signal to noise ratios. We experimented it with three datasets freely available, namely SESA, MIVIA Audio Events and MIVIA Road Events and we achieved 91.43%, 99.62% and 100% recognition rate, respectively. We compared our method with other state of the art methodologies based both on traditional machine learning methodologies and deep learning. The comparison confirms the effectiveness of the proposed approach, which outperforms the existing methods in terms of recognition rate.We experimentally prove that the proposed network is resilient to the noise, has the capability to significantly reduce the false positive rate and is able to generalize in different scenarios. Furthermore, AReN is able to process 5 audio frames per second on a standard CPU and, consequently, it is suitable for real audio surveillance applications.
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