Acoustic shooter localisation using a network of asynchronous acoustic nodes

Hengy, Sébastien; Duffner, P.; DeMezzo, Sebastien; Heck, Stéphane; Gross, Laurent; Naz, Pierre

doi:10.1049/iet-rsn.2016.0029

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…It was shown that the best was from ES-SCFL method in terms of the localization accuracy. Additionally, in [9], asynchronous localization from many nodes over a network has been adopted in order to determine the GPS coordinates of the gunfire. The shockwave and muzzle blast characteristics were used in the process.…”

Section: Introductionmentioning

confidence: 99%

Effect of Sampling Rate Reduction and Signal Filtering for Gunfire Sound Classification with Spectral Vector using ANN

Tangkawanit

Kanprachar

2020

ECTI-EEC

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To identify the type of the shooting gun, the frequency components of the gunfire sound has been utilized as the input to an Artificial Neural Network (ANN) model [1-2] with a promising recognition accuracy. With this method, the required computational resource is large if the sampling frequency is too high. In this research, the sampling frequency for transforming the input gunfire sound into a digital signal is varied from 44.1 kHz down to 4.41 kHz. 6 different types of gunfire sound are considered. Additionally, the effect of applying different types of signal filtering is studied. It is found that only reducing the sampling frequency on the input gunfire sound signal does not deliver a good performance on gunfire sound classification. To obtain a good classification accuracy, signal filtering has to also be applied. With Chebyshev Type II filter and 4.41 kHz sampling frequency, the classification accuracy is all 100% for the practical range of SNR. This impressive classification accuracy comes with a 10-times reduction on the computational resource; that is, from the sampling frequency of 44.1 kHz to 4.41 kHz. The findings from this work can be applied to the gunfire sound classification system with limited computational resource.

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Section: Introductionmentioning

confidence: 99%