Microphone Array Based Surveillance Audio Classification
This work addresses audio classification for surveillance applications, but it is incremental as it primarily compares existing methods.
The paper tackled surveillance audio classification using microphone arrays, testing seven classifiers and two beamforming algorithms with data augmentation and noise, and found that SVM with Delay-and-Sum achieved the best accuracy of 86.0% but had high computational cost, while SGD offered a good alternative with 85.3% accuracy and faster processing.
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).