A vehicular noise surveillance system integrated with vehicle type classification
A vehicular noise surveillance system integrated with vehicle type classification
This paper introduces an ongoing project on the surveillance of noisy vehicles on the road. Noise pollution created by vehicles on urban roads is becoming more severe. To enforce current measures, we developed a vehicular noise surveillance system including a vehicle type classification method. Samples of vehicular noise were recorded on-site using this system. Harmonic features were extracted from each sample based on an average harmonic structure. The k-nearest neighbor (KNN) algorithm was applied to achieve classification accuracies for the passenger car, the van, the lorry, the bus, and the motorbike of 60.66%, 65.38%, 52.99%, 62.02%, and 80%, respectively. This study was motivated by the demand of monitoring noise levels generated by different types of vehicles. The classification method using audio features is independent of lighting condition, thus providing a replacement to machine vision based techniques in vehicle type classification.
Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Gan, Woon-Seng
1936c59c-0552-498c-86a4-20bb81bb561a
Chong, Yong-Kim
bac6ea48-e231-4f3b-a561-98b588c44cfa
Apoorv, Agha
5425f497-cd7e-4c2e-8dfd-89074feed06b
Song, Kin-San
3366902c-14d0-43e8-9aaf-1670cf62cfcc
Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Gan, Woon-Seng
1936c59c-0552-498c-86a4-20bb81bb561a
Chong, Yong-Kim
bac6ea48-e231-4f3b-a561-98b588c44cfa
Apoorv, Agha
5425f497-cd7e-4c2e-8dfd-89074feed06b
Song, Kin-San
3366902c-14d0-43e8-9aaf-1670cf62cfcc
Shi, Chuang, Gan, Woon-Seng, Chong, Yong-Kim, Apoorv, Agha and Song, Kin-San
(2014)
A vehicular noise surveillance system integrated with vehicle type classification.
In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.
IEEE.
5 pp
.
(doi:10.1109/APSIPA.2013.6694290).
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper introduces an ongoing project on the surveillance of noisy vehicles on the road. Noise pollution created by vehicles on urban roads is becoming more severe. To enforce current measures, we developed a vehicular noise surveillance system including a vehicle type classification method. Samples of vehicular noise were recorded on-site using this system. Harmonic features were extracted from each sample based on an average harmonic structure. The k-nearest neighbor (KNN) algorithm was applied to achieve classification accuracies for the passenger car, the van, the lorry, the bus, and the motorbike of 60.66%, 65.38%, 52.99%, 62.02%, and 80%, respectively. This study was motivated by the demand of monitoring noise levels generated by different types of vehicles. The classification method using audio features is independent of lighting condition, thus providing a replacement to machine vision based techniques in vehicle type classification.
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e-pub ahead of print date: 2 January 2014
Venue - Dates:
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2013, , Kaohsiung, Taiwan, 2013-10-29 - 2013-11-01
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Local EPrints ID: 484258
URI: http://eprints.soton.ac.uk/id/eprint/484258
PURE UUID: 47c33a76-6fe5-4a5d-8362-1e14a4608ea5
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Date deposited: 13 Nov 2023 18:52
Last modified: 18 Mar 2024 04:13
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Contributors
Author:
Chuang Shi
Author:
Woon-Seng Gan
Author:
Yong-Kim Chong
Author:
Agha Apoorv
Author:
Kin-San Song
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