Gunshot Recognition using Low Level Features in the Time Domain

Abstract

This paper explores the possibility of using scarcely used time-domain features for the task of gunshot recognition. A set of 11 features derived from temporal characteristics (waveform) of signals is calculated from a mixed dataset of gunshots and non-gunshots. The features leverage the impulsive nature of gunshots and their dissimilarity to other, especially more stationary signals. The paper includes a description of feature extraction, distribution of features and their recognition performance on a selected audio dataset. A subset achieves promising results in comparison with more frequently used spectral-domain features. This makes them a valuable addition to other frequently used features, especially for tasks of impulsive sound recognition.This paper explores the possibility of using scarcely used time-domain features for the task of gunshot recognition. A set of 11 features derived from temporal characteristics (waveform) of signals is calculated from a mixed dataset of gunshots and non-gunshots. The features leverage the impulsive nature of gunshots and their dissimilarity to other, especially more stationary signals. The paper includes a description of feature extraction, distribution of features and their recognition performance on a selected audio dataset. A subset achieves promising results in comparison with more frequently used spectral-domain features. This makes them a valuable addition to other frequently used features, especially for tasks of impulsive sound recognition.