Independent Channel Residual Convolutional Network for Gunshot Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146711" target="_blank" >RIV/00216305:26220/22:PU146711 - isvavai.cz</a>

Výsledek na webu

<a href="https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108" target="_blank" >https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14569/IJACSA.2022.01304108" target="_blank" >10.14569/IJACSA.2022.01304108</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Independent Channel Residual Convolutional Network for Gunshot Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Independent Channel Residual Convolutional Network for Gunshot Detection

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Advanced Computer Science and Applications

ISSN

2156-5570

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

950-958

Kód UT WoS článku

000798606400001

EID výsledku v databázi Scopus

2-s2.0-85130090786