  The work assessed seven classical classifiers and two beamforming algorithms
for detecting surveillance sound events. The tests included the use of AWGN
with -10 dB to 30 dB SNR. Data Augmentation was also employed to improve
algorithms' performance. The results showed that the combination of SVM and
Delay-and-Sum (DaS) scored the best accuracy (up to 86.0\%), but had high
computational cost ($\approx $ 402 ms), mainly due to DaS. The use of SGD also
seems to be a good alternative since it has achieved good accuracy either (up
to 85.3\%), but with quicker processing time ($\approx$ 165 ms).
