Scream and gunshot detection and localization for audio-surveillance systems

This paper describes an audio-based video surveillance system which automatically detects anomalous audio events in a public square, such as screams or gunshots, and lo-calizes the position of the acoustic source, in such a way that a video-camera is steered consequently. The system employs two parallel GMM classifiers for discriminating screams from noise and gunshots from noise, respectively. Each classifier is trained using different features, chosen from a set of both conventional and innovative audio fea-tures. The location of the acoustic source which has pro-duced the sound event is estimated by computing the time difference of arrivals of the signal at a microphone array and using linear-correction least square localization algo-rithm. Experimental results show that our system can detect events with a precision of 93 % at a false rejection rate of 5 % when the SNR is 10dB, while the source direction can be estimated with a precision of one degree. A real-time implementation of the system is going to be installed in a public square of Milan. 1