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These last years, artificial neural networks (ANN) have known a renewed interest since efficient training procedures have emerged to learn the so called deep neural networks (DNN), i.e. ANN with at least two hidden layers. In the same time, the computational auditory scene recognition (CASR) problem which consists in estimating the environment around a device from the received audio signal has been investigated. Most of works which deal with the CASR problem have tried to find well-adapted features for this problem. However, these features are generally combined with a classical classifier. In this paper, we introduce DNN in the CASR field and we show that such networks can provide promising results and perform better than standard classifiers when the same features are used.

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