Urban noise classification for active noise control in residential buildings

In this report, features of the audio data training samples of various class will be extracted to train the classifier model. The model will then predict the class of testing samples of random audio data. The model will also be refined using a Convolutional Neural Network (CNN) to achieve a higher c...

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Bibliographic Details
Main Author:	Chua, MingHui
Other Authors:	Gan Woon Seng
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2020
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/139490
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-139490
record_format	dspace
spelling	sg-ntu-dr.10356-1394902023-07-07T18:24:09Z Urban noise classification for active noise control in residential buildings Chua, MingHui Gan Woon Seng School of Electrical and Electronic Engineering Smart Nation TRANS Lab EWSGAN@ntu.edu.sg Engineering::Electrical and electronic engineering In this report, features of the audio data training samples of various class will be extracted to train the classifier model. The model will then predict the class of testing samples of random audio data. The model will also be refined using a Convolutional Neural Network (CNN) to achieve a higher classification accuracy score. Based on the experiment conducted in this paper, the trained model is able to predict noises to the correct class with an accuracy around 79.2%. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-20T01:49:43Z 2020-05-20T01:49:43Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/139490 en A3086-191 application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Chua, MingHui Urban noise classification for active noise control in residential buildings
description	In this report, features of the audio data training samples of various class will be extracted to train the classifier model. The model will then predict the class of testing samples of random audio data. The model will also be refined using a Convolutional Neural Network (CNN) to achieve a higher classification accuracy score. Based on the experiment conducted in this paper, the trained model is able to predict noises to the correct class with an accuracy around 79.2%.
author2	Gan Woon Seng
author_facet	Gan Woon Seng Chua, MingHui
format	Final Year Project
author	Chua, MingHui
author_sort	Chua, MingHui
title	Urban noise classification for active noise control in residential buildings
title_short	Urban noise classification for active noise control in residential buildings
title_full	Urban noise classification for active noise control in residential buildings
title_fullStr	Urban noise classification for active noise control in residential buildings
title_full_unstemmed	Urban noise classification for active noise control in residential buildings
title_sort	urban noise classification for active noise control in residential buildings
publisher	Nanyang Technological University
publishDate	2020
url	https://hdl.handle.net/10356/139490
_version_	1772826313757818880

Urban noise classification for active noise control in residential buildings

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