A LEARNING BASED APPROACH TO AUDIO SURVEILLANCE IN HOUSEHOLD ENVIRONMENT

摘要

Recently, audio analysis has been proposed by some researchers for surveillance application. Compared with video surveillance, the effectiveness of audio surveillance is not influenced by the occlusions. In this paper, we propose a learning based approach to intelligent audio surveillance in household environment. This approach can be applied to static or mobile audio surveillance systems which work solely or work as the supplements of video surveillance systems. After extracting Mel frequency cepstral coefficients (MFCC) from a 1.0 s waveform, a classifier trained from a labeled dataset using modest AdaBoost is employed to determine whether this waveform is normal or abnormal. The result and simulation show the effectiveness of this approach.