Open AccessEfficient Feature Set Developed for Acoustic Gunshot Detection in Open Space
Author(s) -
Milan Sigmund,
Martin Hrabina
Publication year - 2021
Publication title -
elektronika ir elektrotechnika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.224
H-Index - 26
eISSN - 2029-5731
pISSN - 1392-1215
DOI - 10.5755/j02.eie.28877
Subject(s) - computer science , waveform , normalization (sociology) , pattern recognition (psychology) , feature vector , artificial intelligence , mel frequency cepstrum , artificial neural network , feature (linguistics) , feature extraction , speech recognition , computer vision , telecommunications , radar , linguistics , philosophy , sociology , anthropology
This paper presents an efficient approach to automatic gunshot detection based on a combination of two feature sets: adapted standard sound features and hand-crafted novel features. The standard features are mel-frequency cepstral coefficients adapted for gunshot recognition in terms of uniform gamma-tone filters linearly spaced over the whole frequency range from 0 kHz to 16 kHz. The first 18 coefficients calculated from the 41 filters represent the best set of the optimized cepstral coefficients. The novel features were derived in the time domain from individual significant points of the raw waveform after amplitude normalization. Experiments were performed using single and ensemble neural networks to verify the effectiveness of the novel features for supplementing the standard features. The novelty of the work is the proposed feature combination, which allows to achieve very effective detection of gunshots from hunting weapons using 23 features and a simple neural network. In binary classification, the developed approach achieved an accuracy of 95.02 % in gunshot detection and 98.16 % in disregarding other sounds (i.e., non-gunshot).