On acoustic surveillance of hazardous situations

The present study presents a practical methodology for automatic space monitoring based solely on the perceived acoustic information. We consider the case where atypical situations such as screams, explosions and gunshots take place in a metro station environment. Our approach is based on a two stage recognition schema, each one exploiting HMMs for approximating the density function of the corresponding sound class. The main objective is to detect abnormal events that take place in a noisy environment. A thorough evaluation procedure is carried out under different SNR conditions and we report high detection rates with respect to false alarm and miss probabilities rates.