Classification of interior noise comfort level of Proton model cars using artificial neural network

Car interior noise comfort level classification is one of the most promising sub-fields in automotive research. Car interior noise comfort indicator is developed to help the drivers to keep track of the noise comfort level in the car. Determination of car comfort is important because continuous expo...

Full description

Saved in:

Bibliographic Details
Main Author:	Allan Melvin, Andrew
Format:	Thesis
Language:	English
Subjects:	Vehicle Noise Comfort Index (VNCI) Interior noise comfort Artificial neural network Car interior noise Automobiles > Component and parts Noise control Automobiles > Interior acoustic
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31255/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31255/2/Full%20text.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-31255
record_format	uketd_dc
spelling	my-unimap-312552014-01-16T12:24:47Z Classification of interior noise comfort level of Proton model cars using artificial neural network Allan Melvin, Andrew Car interior noise comfort level classification is one of the most promising sub-fields in automotive research. Car interior noise comfort indicator is developed to help the drivers to keep track of the noise comfort level in the car. Determination of car comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. In this research, a proton model cars noise comfort level classification system has been developed to detect the noise comfort level in cars using artificial neural network. This research focuses on developing a database consisting of car sound samples measured from different proton make cars in stationary and moving state. In the stationary condition, the sound pressure level is measured at 1300 RPM, 2000 RPM and 3000 RPM while in moving condition, the sound is recorded while the car is moving at constant speed from 30 km/h up to 110 km/h. dB Solo equipment is used to measure the noise level inside the car. Subjective test is conducted to find the jury’s evaluation for the specific sound sample. The data is preprocessed and features are extracted from the signal frames. The correlation between the subjective and the objective evaluation is also tested. The feature set is then feed to the neural network model to classify the comfort level. The respective index is displayed at the designed Graphical User Interface (GUI). Experimental results show that the use of proposed Composite Feature yields a better classification accuracy compared to the conventional feature extraction method. The Spectral Composite Feature gives the highest classification accuracy of 94.21%. Universiti Malaysia Perlis (UniMAP) 2012 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/31255 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31255/1/Page%201-24.pdf fb3d233ca097d9769f86ca673aa19644 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31255/2/Full%20text.pdf f0884edb3d7594aa6c10591f75d5ef8d http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31255/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Vehicle Noise Comfort Index (VNCI) Interior noise comfort Artificial neural network Car interior noise Automobiles -- Component and parts Noise control Automobiles -- Interior acoustic School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Vehicle Noise Comfort Index (VNCI) Interior noise comfort Artificial neural network Car interior noise Automobiles -- Component and parts Noise control Automobiles -- Interior acoustic
spellingShingle	Vehicle Noise Comfort Index (VNCI) Interior noise comfort Artificial neural network Car interior noise Automobiles -- Component and parts Noise control Automobiles -- Interior acoustic Allan Melvin, Andrew Classification of interior noise comfort level of Proton model cars using artificial neural network
description	Car interior noise comfort level classification is one of the most promising sub-fields in automotive research. Car interior noise comfort indicator is developed to help the drivers to keep track of the noise comfort level in the car. Determination of car comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. In this research, a proton model cars noise comfort level classification system has been developed to detect the noise comfort level in cars using artificial neural network. This research focuses on developing a database consisting of car sound samples measured from different proton make cars in stationary and moving state. In the stationary condition, the sound pressure level is measured at 1300 RPM, 2000 RPM and 3000 RPM while in moving condition, the sound is recorded while the car is moving at constant speed from 30 km/h up to 110 km/h. dB Solo equipment is used to measure the noise level inside the car. Subjective test is conducted to find the jury’s evaluation for the specific sound sample. The data is preprocessed and features are extracted from the signal frames. The correlation between the subjective and the objective evaluation is also tested. The feature set is then feed to the neural network model to classify the comfort level. The respective index is displayed at the designed Graphical User Interface (GUI). Experimental results show that the use of proposed Composite Feature yields a better classification accuracy compared to the conventional feature extraction method. The Spectral Composite Feature gives the highest classification accuracy of 94.21%.
format	Thesis
author	Allan Melvin, Andrew
author_facet	Allan Melvin, Andrew
author_sort	Allan Melvin, Andrew
title	Classification of interior noise comfort level of Proton model cars using artificial neural network
title_short	Classification of interior noise comfort level of Proton model cars using artificial neural network
title_full	Classification of interior noise comfort level of Proton model cars using artificial neural network
title_fullStr	Classification of interior noise comfort level of Proton model cars using artificial neural network
title_full_unstemmed	Classification of interior noise comfort level of Proton model cars using artificial neural network
title_sort	classification of interior noise comfort level of proton model cars using artificial neural network
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31255/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31255/2/Full%20text.pdf
_version_	1747836777564995584

Classification of interior noise comfort level of Proton model cars using artificial neural network

Similar Items