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Moving vehicle noise classification using backpropagation algorithm

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Main Authors: Norasmadi, Abdul Rahim, Paulraj, Murugesa Pandiyan, Assoc. Prof., Abdul Hamid, Adom, Assoc. Prof. Dr., Sundararaj, Sathishkumar
Other Authors: norasmadi@ieee.org
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2011
Subjects:
Backpropagation
Feature extraction
Hearing impaired
Noise classification
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/10432
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spelling my.unimap-104322011-01-07T04:45:25Z Moving vehicle noise classification using backpropagation algorithm Norasmadi, Abdul Rahim Paulraj, Murugesa Pandiyan, Assoc. Prof. Abdul Hamid, Adom, Assoc. Prof. Dr. Sundararaj, Sathishkumar norasmadi@ieee.org paul@unimap.edu.my abdhamid@unimap.edu.my sathishy2j@yahoo.com Backpropagation Feature extraction Hearing impaired Noise classification Link to publisher's homepage at http://ieeexplore.ieee.org/ The hearing impaired is afraid of walking along a street and living a life alone. Since it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoors. The sound produced by moving vehicle in outdoor situation cannot be moderate wisely by profoundly deaf people. They also cannot distinguish the type and the distance of any moving vehicle approaching from their behind. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. In this paper, a simple system that identifies the type and distance of a moving vehicle using artificial neural network has been proposed. The noises emanated from moving vehicles along the roadside were recorded along with the type and distance of moving vehicles. Simple feature extraction algorithm for extracting the feature from noise emanated by the moving vehicle has been made using frequency analysis approach. A onethird- octave filter bands is used for getting the important signatures from the emanated noise. The extracted features are associated with the type and distance of the moving vehicle and a simple neural network model is developed. The developed neural network model is tested for its validity. 2011-01-07T04:45:22Z 2011-01-07T04:45:22Z 2010-05-21 Working Paper p. 1-6 978-1-4244-7121-8 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5545231 http://hdl.handle.net/123456789/10432 en Proceedings of the 6th International Colloquium on Signal Processing and Its Applications (CSPA) 2010 Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Backpropagation
Feature extraction
Hearing impaired
Noise classification
spellingShingle Backpropagation
Feature extraction
Hearing impaired
Noise classification
Norasmadi, Abdul Rahim
Paulraj, Murugesa Pandiyan, Assoc. Prof.
Abdul Hamid, Adom, Assoc. Prof. Dr.
Sundararaj, Sathishkumar
Moving vehicle noise classification using backpropagation algorithm
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 norasmadi@ieee.org
author_facet norasmadi@ieee.org
Norasmadi, Abdul Rahim
Paulraj, Murugesa Pandiyan, Assoc. Prof.
Abdul Hamid, Adom, Assoc. Prof. Dr.
Sundararaj, Sathishkumar
format Working Paper
author Norasmadi, Abdul Rahim
Paulraj, Murugesa Pandiyan, Assoc. Prof.
Abdul Hamid, Adom, Assoc. Prof. Dr.
Sundararaj, Sathishkumar
author_sort Norasmadi, Abdul Rahim
title Moving vehicle noise classification using backpropagation algorithm
title_short Moving vehicle noise classification using backpropagation algorithm
title_full Moving vehicle noise classification using backpropagation algorithm
title_fullStr Moving vehicle noise classification using backpropagation algorithm
title_full_unstemmed Moving vehicle noise classification using backpropagation algorithm
title_sort moving vehicle noise classification using backpropagation algorithm
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/10432
_version_ 1643789901485834240
score 13.222552

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