Investigation of noise features of different types of vehicles using an array of low-cost web cameras

Sound of moving vehicle provides important information of noise features of particular vehicles. In this final year project report, I will introduce using an array of low cost web/smart phone/video camera to finding vehicles noise and classification of vehicles by extracted raw data from vehicles no...

Full description

Saved in:
Bibliographic Details
Main Author: Hasan, Md Mehedi.
Other Authors: Chong Yong Kim
Format: Final Year Project
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/10356/54596
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-54596
record_format dspace
spelling sg-ntu-dr.10356-545962023-07-07T17:18:16Z Investigation of noise features of different types of vehicles using an array of low-cost web cameras Hasan, Md Mehedi. Chong Yong Kim Gan Woon Seng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Sound of moving vehicle provides important information of noise features of particular vehicles. In this final year project report, I will introduce using an array of low cost web/smart phone/video camera to finding vehicles noise and classification of vehicles by extracted raw data from vehicles noise using digital signal processing concept, originally used in human hearing reorganization (20Hz-20 kHz) to model the sound frequency distribution. In this paper will show that, this method can be very reliable to find vehicle sound classification if raw data are properly collect from moving vehicle and categorized them. I treat sampling frequency 48 kHz to reduce any aliasing or unwanted noise since both video camera and smart-phone camera frequency response range 20Hz to 20 kHz. In this frequency range, author analysis frequency spectrum for each type of vehicles (Motorcar, Lorry, and Motorbike) in time domain and frequency domain. A collection of vehicle sound sampled (real time traffic flow) use for further data analysis for data record. The peak of frequency spectrum is most important part of this experiment, where vehicles sound shows that audio/video capturing by video camera motorbike and lorry produces maximum level of noise and car is the lowest. Conversely, vehicle sound spectrum shows that audio/video capturing by smart phone camera work at low frequency (0-1000Hz), where lorry and motorcar produces maximum noise and motorbike at average level. After comparison among different set of data to conclude different types of vehicle produce different noise features. Author hope this experiment could be very useful to understand noise features of vehicles and can be use as a prototype for future research in vehicle noise investigation. Bachelor of Engineering 2013-06-24T06:31:10Z 2013-06-24T06:31:10Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/54596 en Nanyang Technological University 64 p. application/pdf application/pdf application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Hasan, Md Mehedi.
Investigation of noise features of different types of vehicles using an array of low-cost web cameras
description Sound of moving vehicle provides important information of noise features of particular vehicles. In this final year project report, I will introduce using an array of low cost web/smart phone/video camera to finding vehicles noise and classification of vehicles by extracted raw data from vehicles noise using digital signal processing concept, originally used in human hearing reorganization (20Hz-20 kHz) to model the sound frequency distribution. In this paper will show that, this method can be very reliable to find vehicle sound classification if raw data are properly collect from moving vehicle and categorized them. I treat sampling frequency 48 kHz to reduce any aliasing or unwanted noise since both video camera and smart-phone camera frequency response range 20Hz to 20 kHz. In this frequency range, author analysis frequency spectrum for each type of vehicles (Motorcar, Lorry, and Motorbike) in time domain and frequency domain. A collection of vehicle sound sampled (real time traffic flow) use for further data analysis for data record. The peak of frequency spectrum is most important part of this experiment, where vehicles sound shows that audio/video capturing by video camera motorbike and lorry produces maximum level of noise and car is the lowest. Conversely, vehicle sound spectrum shows that audio/video capturing by smart phone camera work at low frequency (0-1000Hz), where lorry and motorcar produces maximum noise and motorbike at average level. After comparison among different set of data to conclude different types of vehicle produce different noise features. Author hope this experiment could be very useful to understand noise features of vehicles and can be use as a prototype for future research in vehicle noise investigation.
author2 Chong Yong Kim
author_facet Chong Yong Kim
Hasan, Md Mehedi.
format Final Year Project
author Hasan, Md Mehedi.
author_sort Hasan, Md Mehedi.
title Investigation of noise features of different types of vehicles using an array of low-cost web cameras
title_short Investigation of noise features of different types of vehicles using an array of low-cost web cameras
title_full Investigation of noise features of different types of vehicles using an array of low-cost web cameras
title_fullStr Investigation of noise features of different types of vehicles using an array of low-cost web cameras
title_full_unstemmed Investigation of noise features of different types of vehicles using an array of low-cost web cameras
title_sort investigation of noise features of different types of vehicles using an array of low-cost web cameras
publishDate 2013
url http://hdl.handle.net/10356/54596
_version_ 1772826524085387264