This thesis aims to determine how effective it is to use advanced techniques in Deep Learning for the classification of environmental sounds, focusing mainly on the recognition of human activities, in the presence of background noises of different nature, and highlighting their performance and robustness in different conditions. The study uses four Deep Learning models and an ESC-50 reference dataset, which has been reduced to 19 classes by selecting only domestic and human sounds. Some of the selected models had already been pre-trained with AudioSet, a larger dataset of environmental sounds. The models were exposed to various signal-to-noise ratio (SNR) levels.