Abstract
The gunshot event localization and classification have numerous real-time applications. The study is also useful for steering the video camera and guns in the directed direction. This paper proposes a framework that can be used for a surveillance system to accurately localize and classify the type of gunshots impregnated with wind noise. The main contribution of this paper is the localization of the gunshot for the very first time using Hadamard product with wavelet de-noising in windy conditions. We have evaluated our framework on airborne gunshots acoustic dataset, and a derived (simulated) sound dataset, as an offline scenario, using four microphones' geometry. For localization, the proposed system outperformed with an accuracy of 99.95%. The other contribution is a sensitivity-based comprehensive examination of gunshot sound signals, with normal to strong wind noise of varying SNRs, for machine learning and deep learning classifiers to categorize the type of gunshots. For classification, it has been found, not known before for the gunshots dataset, that ELM is robust for original, normal, and strong windy environments with an accuracy of 93.01%, 91.61%, and 88.11% respectively with the threshold SNR. A comprehensive comparison of recent techniques with the proposed approach has also been added.