Scream and gunshot detection in noisy environments

This paper describes an audio event detection system which au-tomatically classifies an audio event as ambient noise, scream or gunshot. The classification system uses two parallel GMM clas-sifiers for discriminating screams from noise and gunshots from noise. Each classifier is trained using different features, appropri-ately chosen from a set of 47 audio features, which are selected according to a 2-step process. First, feature subsets of increasing size are assembled using filter selection heuristics. Then, a clas-sifier is trained and tested with each feature subset. The obtained classification performance is used to determine the optimal feature vector dimension. This allows a noticeable speed-up w.r.t. wrap-per feature selection methods. In order to validate the proposed detection algorithm, we carried out extensive experiments on a rich set of gunshots and screams mixed with ambient noise at different SNRs. Our results demonstrate that the system is able to guarantee a precision of 90 % at a false rejection rate of 8% 1