NDLI: Moving vehicle noise classification using multiple classifiers

Content Provider	IEEE Xplore Digital Library
Author	Rahim, N.A. Paulraj, M.P. Adom, A.H. Kumar, S.S.
Copyright Year	2011
Description	Author affiliation: School of Mechatronic Engineering, Ulu Pauh Campus, 02600 Arau, Perlis, MALAYSIA (Rahim, N.A.; Paulraj, M.P.; Adom, A.H.; Kumar, S.S.)
Abstract	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 moderated wisely by profoundly hearing impaired community. They also cannot distinguish the type and the distance of any moving vehicle approaching from their behind. In this paper, a simple system that identifies the type and distance of a moving vehicle using artificial neural network has been proposed. The noise emanated from a moving vehicle along the roadside was recorded together with its type and position. Using frequency-domain approach, simple feature extraction algorithm for extracting the feature from the noise emanated by the moving vehicle has been developed. One-third-octave filter bands were used and the significant features from the emanated noise were extracted. The extracted features were associated with the type and zone of the moving vehicle and a multiple classifier system (MCS) based on neural network model has been developed. The developed MCS is tested for its validity.
Starting Page	105
Ending Page	110
File Size	798121
Page Count	6
File Format	PDF
ISBN	9781467300995
e-ISBN	9781467301022
DOI	10.1109/SCOReD.2011.6148717
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-19
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Multiple Classifier System Vehicle Noise Classification Noise Neurons Multilayer Perceptron Feature extraction Band pass filters Voting System Vehicles
Content Type	Text
Resource Type	Article

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