Gunshot detection in noisy environments

Freire, Izabela Lyon; Jose, Jose

doi:10.14209/sbrt.2010.92

Cited by 30 publications

(11 citation statements)

References 12 publications

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“…As previously noted, a gunshot surveillance system must be able to detect an impulsive signal and to identify if it is a muzzle blast component, a shockwave component, or none of them. There is a vast literature available about this matter [41,42,43,44,45,46].…”

Section: Doa Estimation and Shooter Localizationmentioning

confidence: 99%

“…A detection scheme based on correlation against a template is proposed in Reference [43], where the authors claim that the method could be implemented by a low power consuming hardware. Correlation against a template is also addressed in Reference [44], where it is compared against classical algorithms usually used in speech processing; their results conclude that correlation matches the performance of those algorithms, especially in noisy environments. In Reference [45], linear predictive coding (LPC) coefficients are combined with template matching to increase the performance of gunshot detection systems, especially regarding false-positive errors.…”

Section: Doa Estimation and Shooter Localizationmentioning

confidence: 99%

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Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Serrenho

Apolinario

Ramos

et al. 2019

Sensors

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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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Section: Doa Estimation and Shooter Localizationmentioning

confidence: 99%

Section: Doa Estimation and Shooter Localizationmentioning

confidence: 99%

Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Serrenho

Apolinario

Ramos

et al. 2019

Sensors

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“…Recently, Sen et al proposed a new feature extraction technique coming from a new transformation which is based on the Nyquist filter bank and achieved significant result in speaker identification [11]. Besides, feature sets extracted from Linear Predictive coefficients (LPC) and mel-frequency cepstral coefficient (MFCC) also have powerful descriptive capability which are used frequently in gunshot detection [12], audio clips classification [13], environment sound recognition [14], and emotion recognition from speech [15].…”

Section: B Audio Featuresmentioning

confidence: 99%

Identifying Microphone from Noisy Recordings by Using Representative Instance One Class-Classification Approach

Vu¹,

Liu²,

Yang³

et al. 2012

JNW

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Rapid growth of technical developments has created huge challenges for microphone forensics - a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. In addition, we propose a representative instance classification framework (RICF) that can effectively improve performance of OCC algorithms for recording signal with noise. Experiment results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis

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“…The motivation of this work stems from the fact that datasets containing sounds closely related to gunshot sounds are not readily available for research purposes. Although a great amount of work has been presented in the detection of gunshot sounds [3][4][5][6][7][8][9][10][11][12][13][14][15][16], comparative analyses of similar audio events are not as detailed, though in the literature, researchers carried out comparative analyses of similar sounding events to gunshot audio events with abrupt changes in energy, such as door slams, claps, and firecrackers [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study

Singh

Zhuang

Pawani

2021

Sensors

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Distinguishing between a dangerous audio event like a gun firing and other non-life-threatening events, such as a plastic bag bursting, can mean the difference between life and death and, therefore, the necessary and unnecessary deployment of public safety personnel. Sounds generated by plastic bag explosions are often confused with real gunshot sounds, by either humans or computer algorithms. As a case study, the research reported in this paper offers insight into sounds of plastic bag explosions and gunshots. An experimental study in this research reveals that a deep learning-based classification model trained with a popular urban sound dataset containing gunshot sounds cannot distinguish plastic bag pop sounds from gunshot sounds. This study further shows that the same deep learning model, if trained with a dataset containing plastic pop sounds, can effectively detect the non-life-threatening sounds. For this purpose, first, a collection of plastic bag-popping sounds was recorded in different environments with varying parameters, such as plastic bag size and distance from the recording microphones. The audio clips’ duration ranged from 400 ms to 600 ms. This collection of data was then used, together with a gunshot sound dataset, to train a classification model based on a convolutional neural network (CNN) to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. A comparison between two feature extraction methods, the Mel-frequency cepstral coefficients (MFCC) and Mel-spectrograms, was also done. Experimental studies conducted in this research show that once the plastic bag pop sounds are injected into model training, the CNN classification model performs well in distinguishing actual gunshot sounds from plastic bag sounds.

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Gunshot detection in noisy environments

Cited by 30 publications

References 12 publications

Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Identifying Microphone from Noisy Recordings by Using Representative Instance One Class-Classification Approach

Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study

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