2020/05/22 by Dimitri Leandro de Oliveira Silva, Silva, Dimitri Leandro de Oliveira, Tito Spadini +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Animal Vocal Communication and Behavior #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.11348
openalex publication_date 2020/05/22 · openalex created_date 2020/05/29 · openalex updated_date 2026/07/28
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).