Independent Channel Residual Convolutional Network for Gunshot Detection

Independent Channel Residual Convolutional Network for Gunshot Detection

WoS Article

The main purpose of this work is to propose a robust approach for dangerous sound events detection (e.g. gunshots) to improve recent surveillance systems. Despite the fact that the detection and classification of different sound events has a long history in signal processing, the analysis of environmental sounds is still challenging. The most recent works aim to prefer the time-frequency 2-D representation of sound as input to feed convolutional neural networks. This paper includes an analysis of known architectures as well as a newly proposed Independent Channel Residual Convolutional Network architecture based on standard residual blocks. Our approach consists of processing three different types of features in the individual channels. The UrbanSound8k and the Free Firearm Sound Library audio datasets are used for training and testing data generation, achieving a 98 % F1 score. The model was also evaluated in the wild using manually annotated movie audio track, achieving a 44 % F1 score, which is not too high but still better than other state-of-the-art techniques.

The main purpose of this work is to propose a robust approach for dangerous sound events detection (e.g. gunshots) to improve recent surveillance systems. Despite the fact that the detection and classification of different sound events has a long history in signal processing, the analysis of environmental sounds is still challenging. The most recent works aim to prefer the time-frequency 2-D representation of sound as input to feed convolutional neural networks. This paper includes an analysis of known architectures as well as a newly proposed Independent Channel Residual Convolutional Network architecture based on standard residual blocks. Our approach consists of processing three different types of features in the individual channels. The UrbanSound8k and the Free Firearm Sound Library audio datasets are used for training and testing data generation, achieving a 98 % F1 score. The model was also evaluated in the wild using manually annotated movie audio track, achieving a 44 % F1 score, which is not too high but still better than other state-of-the-art techniques.

Acoustic signal processing; gunshot detection systems; audio signal analysis; machine learning; deep learning; residual networks

Acoustic signal processing; gunshot detection systems; audio signal analysis; machine learning; deep learning; residual networks

BAJZÍK, J.; PŘINOSIL, J.; JARINA, R.; MEKYSKA, J.

2023

01.05.2022

Science and Information Organization

2156-5570

International Journal of Advanced Computer Science and Applications

13

4

United States of America

950

958

9

https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108

http://hdl.handle.net/11012/209174

Paper_108