Gunshot Detection Using Convolutional Neural Networks

Bajzík, Jakub; Přinosil, Jiří; Koniar, Dušan

doi:10.1109/ieeeconf49502.2020.9141621

Cited by 16 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An application of sound classification and localization is gunshot classification and localization, which focuses on gunshots as sound sources, and several systems for detecting gunshots have been proposed for achieving social safety [16], [17], [18]. A major obstacle in the gunshot classification and localization task is the data acquisition problem.…”

Section: Related Workmentioning

confidence: 99%

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

Park,

Cho,

Sim

et al. 2022

Preprint

View full text Add to dashboard Cite

Recently, deep learning-based methods have drawn huge attention due to their simple yet high performance without domain knowledge in sound classification and localization tasks. However, a lack of gun sounds in existing datasets has been a major obstacle to implementing a support system to spot criminals from their gunshots by leveraging deep learning models. Since the occurrence of gunshot is rare and unpredictable, it is impractical to collect gun sounds in the real world. As an alternative, gun sounds can be obtained from an FPS game that is designed to mimic real-world warfare. The recent FPS game offers a realistic environment where we can safely collect gunshot data while simulating even dangerous situations. By exploiting the advantage of the game environment, we construct a gunshot dataset, namely BGG, for the firearm classification and gunshot localization tasks. The BGG dataset consists of 37 different types of firearms, distances, and directions between the sound source and a receiver. We carefully verify that the in-game gunshot data has sufficient information to identify the location and type of gunshots by training several sound classification and localization baselines on the BGG dataset. Afterward, we demonstrate that the accuracy of real-world firearm classification and localization tasks can be enhanced by utilizing the BGG dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

Park,

Cho,

Sim

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…sound events with abrupt changes in energy, such as door slams, claps, and recrackers [10][11][12][13][14][15]. Although it will take a very long time to capture data on a multitude of surroundings, the environments mentioned above are representative to expose the ML algorithm to a wide range of features.…”

Section: Environmentmentioning

confidence: 99%

“…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%

“…The work in [16] provided the comparison of impulsive sounds which includes the explosion of a balloon, clap and the sound of plosive consonants such a [p], [t], and [k]. The authors of [10][11][12][13][14][15][16] all discuss the detection of gunshot sounds in comparison to other sounds; however, they did not provide a database of the non-gunshot sounds readily available for further research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Singh

Zhuang

Pawani

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…Some researchers tested gunshot recognition using methods that were primarily developed for image recognition. For instance, the study in [8] describes the successful use of two-dimensional sound visualizations based on a spectrogram, MFCC, and a self-similarity matrix showing signal correlation. An overview of successful approaches developed by academics can be found in the proceedings resulting from the competition "Detection and Classification of Acoustic Scenes and Events (DCASE)" [9], a challenge that invited the authors to compare their sound detection algorithms where gunshots were the target sounds.…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature Set Developed for Acoustic Gunshot Detection in Open Space

Sigmund

Hrabina

2021

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

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).

show abstract

Gunshot Detection Using Convolutional Neural Networks

Cited by 16 publications

References 13 publications

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

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

Efficient Feature Set Developed for Acoustic Gunshot Detection in Open Space

Contact Info

Product

Resources

About