The goal of the master thesis is to study the task of Sound Event Classification using Deep Neural Networks in an end-to-end approach. Sound Event Classification it is a multi-label classification problem of sound sources originated from everyday environments. An automatic system for it would many applications, for example, it could help users of hearing devices to understand their surroundings or enhance robot navigation systems. The end-to-end approach consists in systems that learn directly from data, not from features, and it has been recently applied to audio and its results are remarkable. Even though the results do not show an improvement over standard approaches, the contribution of this thesis is an exploration of deep learning architectures which can be useful to understand how networks process audio.