Consistent DOA estimation of heavily noisy gunshot signals using a microphone array

Borzino, A. M. C. R.; Apolinario, J.A.; Campos, Marcello L. R. de

doi:10.1049/iet-rsn.2016.0015

Cited by 18 publications

(14 citation statements)

References 26 publications

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“…Apart from identifying the type of gunfire, the localization of the gunfire has also been in the focus of many research works. A technique called DOA (Degree of Arrival) has been adopted [8] in order to receive the signal from the array microphone. The input signal was estimated by using three methods; that is, ES (Exhaustive Search), SCFL (Searching Consistent Fundamental Loop), and ES-SCFL.…”

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%

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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“…The DoA estimation, as seem in Table II, presented a lower average error (5.0 • ) and the number of pairs (n), as expected, varies with the shooting position (distance). These results suggest that measuring the SNR and deciding based on its estimated value which value of n to use, as in the decision tree employed in [20], would be the best for a more accurate solution. Yet, a better DoA estimation does not always yield an accurate position estimate which is heavily dependent on the recording geometry (observation points or quadcopter trajectory).…”

Section: B Experimental Resultsmentioning

confidence: 97%

Bearings-only aerial shooter localization using a microphone array mounted on a drone

Fernandes

Apolinario

Ramos

2017

2017 IEEE 8th Latin American Symposium on Circuits &Amp; Systems (LASCAS)

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Estimating the direction of arrival (DoA) of an audio signal from an aerial platform gives way to estimating the source localization. This paper addresses the problem of airborne shooter localization using a microphone array mounted on a drone. In this scenario, the noise of the propellers poses a great level of difficulty in estimating the DoA of the gunshot signals owing to low levels of SNR. This, combined with the fact that a moving drone records multiple gunshots at different positions, have discouraged the use of drones for shooter localization. Based on real gunshot signals recorded at a shooting site, we explore the advantages and limitations of using a drone for the task of audio surveillance and gunshot detection and localization.

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“…We need to be cautious when choosing the number of pairs of microphones to be used, parameter “ n ” in ES( n ), once it can generate ill-conditioned matrices [55]. The appropriate choice for “ n ” can be obtained according to a decision tree as done in Reference [6].…”

Section: Doa Estimation and Shooter Localizationmentioning

confidence: 99%

“…In this paper, median filtering is used to enhance the collected acoustic gunshot signals. In Reference [6], an algorithm that optimizes DoA estimation using exhausive search through consistent fundamental loops is introduced. This method, in an attempt to have the best approach for the level of noise of audio signals, is a combination of standard DoA estimation, Exhaustive Search (ES) [7], and consistent fundamental loop [6].…”

Section: Introductionmentioning

confidence: 99%

Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Serrenho

Apolinario

Ramos

et al. 2019

Sensors

Self Cite

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Unmanned aerial vehicles (UAV) are growing in popularity, and recent technological advances are fostering the development of new applications for these devices. This paper discusses the use of aerial drones as a platform for deploying a gunshot surveillance system based on an array of microphones. Notwithstanding the difficulties associated with the inherent additive noise from the rotating propellers, this application brings an important advantage: the possibility of estimating the shooter position solely based on the muzzle blast sound, with the support of a digital map of the terrain. This work focuses on direction-of-arrival (DoA) estimation methods applied to audio signals obtained from a microphone array aboard a flying drone. We investigate preprocessing and different DoA estimation techniques in order to obtain the setup that performs better for the application at hand. We use a combination of simulated and actual gunshot signals recorded using a microphone array mounted on a UAV. One of the key insights resulting from the field recordings is the importance of drone positioning, whereby all gunshots recorded in a region outside a cone open from the gun muzzle presented a hit rate close to 96%. Based on experimental results, we claim that reliable bearing estimates can be achieved using a microphone array mounted on a drone.

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Consistent DOA estimation of heavily noisy gunshot signals using a microphone array

Cited by 18 publications

References 26 publications

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

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

Bearings-only aerial shooter localization using a microphone array mounted on a drone

Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

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