  Due to the growing demand for improving surveillance capabilities in smart
cities, systems need to be developed to provide better monitoring capabilities
to competent authorities, agencies responsible for strategic resource
management, and emergency call centers. This work assumes that, as a
complementary monitoring solution, the use of a system capable of detecting the
occurrence of sound events, performing the Sound Events Recognition (SER) task,
is highly convenient. In order to contribute to the classification of such
events, this paper explored several classifiers over the SESA dataset, composed
of audios of three hazard classes (gunshots, explosions, and sirens) and a
class of casual sounds that could be misinterpreted as some of the other
sounds. The best result was obtained by SGD, with an accuracy of 72.13% with
6.81 ms classification time, reinforcing the viability of such an approach.
