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arXiv · 2005.11348

Microphone Array Based Surveillance Audio Classification

Dimitri Leandro de Oliveira Silva·Tito Spadini·Ricardo Suyama
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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 ($\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).

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BibTeXRIS

Dimitri Leandro de Oliveira Silva, Tito Spadini, Ricardo Suyama. 2020-05-22. Microphone Array Based Surveillance Audio Classification. https://arxiv.org/abs/2005.11348

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