论文标题
基于麦克风阵列的监视音频分类
Microphone Array Based Surveillance Audio Classification
论文作者
论文摘要
这项工作评估了七个经典分类器和两种用于检测监视声音事件的波束形成算法。测试包括使用-10 dB至30 dB SNR的AWGN。还采用了数据增强来提高算法的性能。结果表明，SVM和延迟和-MUM（DAS）的组合得分最佳（最高为86.0 \％），但计算成本高（$ \ $ 402毫秒），主要是由于DAS。 SGD的使用似乎也是一个不错的选择，因为它已经达到了良好的准确性（最高为85.3 \％），但处理时间更快（$ \ $ \ $ 165毫秒）。
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).
