Microphone Array Based Surveillance Audio Classification
Audio and Speech Processing
2020-05-26 v1 Machine Learning
Signal Processing
Machine Learning
Abstract
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 ( 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 ( 165 ms).
Keywords
Cite
@article{arxiv.2005.11348,
title = {Microphone Array Based Surveillance Audio Classification},
author = {Dimitri Leandro de Oliveira Silva and Tito Spadini and Ricardo Suyama},
journal= {arXiv preprint arXiv:2005.11348},
year = {2020}
}