A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN

In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event from audio information, and the use of audio surveillance syst...

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Autores principales:	Liyan Luo, Liujun Zhang, Mei Wang, Zhenghong Liu, Xin Liu, Ruibin He, Ye Jin
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Deep learning polyphonic sound event detection feature aggregation monitoring platform Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/00625346e21a438491f18f5330021399
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spelling	oai:doaj.org-article:00625346e21a438491f18f53300213992021-11-18T00:04:45ZA System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN2169-353610.1109/ACCESS.2021.3123970https://doaj.org/article/00625346e21a438491f18f53300213992021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592754/https://doaj.org/toc/2169-3536In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event from audio information, and the use of audio surveillance systems can greatly improve the robustness and reliability of event detection. In this paper, a novel system for the detection of polyphonic urban noise is proposed for on-campus audio surveillance. The system aggregates different acoustic features to improve the classification accuracy of urban noise. A combination model composed of a capsule neural network (CapsNet) and recurrent neural network (RNN) is employed as the classifier. CapsNet overcomes some limitations of convolutional neural networks (CNNs), such as the loss of position information after max-pooling, and the RNN mainly models the temporal dependency of context information. The combination of these networks further improves the accuracy and robustness of polyphonic sound events detection. Moreover, a monitoring platform is designed to visualize noise maps and acoustic event information. The deployment architecture of the system is used in real environments, and experiments were also conducted on two public datasets. The results demonstrate that the proposed method is superior to existing state-of-art methods for the polyphonic sound event detection task.Liyan LuoLiujun ZhangMei WangZhenghong LiuXin LiuRuibin HeYe JinIEEEarticleDeep learningpolyphonic sound event detectionfeature aggregationmonitoring platformElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147900-147913 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning polyphonic sound event detection feature aggregation monitoring platform Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Deep learning polyphonic sound event detection feature aggregation monitoring platform Electrical engineering. Electronics. Nuclear engineering TK1-9971 Liyan Luo Liujun Zhang Mei Wang Zhenghong Liu Xin Liu Ruibin He Ye Jin A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN
description	In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event from audio information, and the use of audio surveillance systems can greatly improve the robustness and reliability of event detection. In this paper, a novel system for the detection of polyphonic urban noise is proposed for on-campus audio surveillance. The system aggregates different acoustic features to improve the classification accuracy of urban noise. A combination model composed of a capsule neural network (CapsNet) and recurrent neural network (RNN) is employed as the classifier. CapsNet overcomes some limitations of convolutional neural networks (CNNs), such as the loss of position information after max-pooling, and the RNN mainly models the temporal dependency of context information. The combination of these networks further improves the accuracy and robustness of polyphonic sound events detection. Moreover, a monitoring platform is designed to visualize noise maps and acoustic event information. The deployment architecture of the system is used in real environments, and experiments were also conducted on two public datasets. The results demonstrate that the proposed method is superior to existing state-of-art methods for the polyphonic sound event detection task.
format	article
author	Liyan Luo Liujun Zhang Mei Wang Zhenghong Liu Xin Liu Ruibin He Ye Jin
author_facet	Liyan Luo Liujun Zhang Mei Wang Zhenghong Liu Xin Liu Ruibin He Ye Jin
author_sort	Liyan Luo
title	A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN
title_short	A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN
title_full	A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN
title_fullStr	A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN
title_full_unstemmed	A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN
title_sort	system for the detection of polyphonic sound on a university campus based on capsnet-rnn
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/00625346e21a438491f18f5330021399
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A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN

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