  In this paper, we propose a new Sound Event Classification (SEC) method which
is inspired in recent works for out-of-distribution detection. In our method,
we analyse all the activations of a generic CNN in order to produce feature
representations using Gram Matrices. The similarity metrics are evaluated
considering all possible classes, and the final prediction is defined as the
class that minimizes the deviation with respect to the features seeing during
training. The proposed approach can be applied to any CNN and our experimental
evaluation of four different architectures on two datasets demonstrated that
our method consistently improves the baseline models.
