Improving efficiency and reliability of gunshot detection systems

In this paper, we focus on setting up a gunshot detection system with high detection performance, robustness to noise and low computational complexity. To achieve these objectives, we formulate a two-stage approach with a less costly impulsive event detection framework followed by a relatively more complex gunshot recognition stage. To improve detection performance of the gunshot recognition stage, we propose a template matching measure in conjunction with the eighth order linear predictive coding coefficients to train a support vector machine classifier. Using an extensive audio database, we were able to achieve a better gunshot recognition performance than with the well-known existing features used for gunshot detection.