Machine learning / deep learning approach to soundscape evaluations

Masking is the addition of sounds to soundscapes or noise-polluted areas. These additional sounds are known as “maskers”. Soundscape augmentation is a method that involves the addition of “maskers” to a soundscape. It is a noise mitigation method that aims to improve the overall soundscape per...

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Main Author: Phang, Rachel Rei Xuan
Other Authors: Gan Woon Seng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167424
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1674242023-07-07T15:43:36Z Machine learning / deep learning approach to soundscape evaluations Phang, Rachel Rei Xuan Gan Woon Seng School of Electrical and Electronic Engineering EWSGAN@ntu.edu.sg Engineering::Electrical and electronic engineering Masking is the addition of sounds to soundscapes or noise-polluted areas. These additional sounds are known as “maskers”. Soundscape augmentation is a method that involves the addition of “maskers” to a soundscape. It is a noise mitigation method that aims to improve the overall soundscape perception or quality. Many studies have used such techniques to improve the perception of a soundscape. However, the studies conducted have some limitations. The choice of maskers used in those studies are often limited to a single type of masker and are inflexible to real-time soundscapes. The method for selecting maskers also tends to be dependent on experts. This project will be using a machine learning/deep learning approach to select maskers from the given masker database for a soundscape, which can instantaneously and independently predict a suitable masker for that soundscape to create an overall pleasant soundscape. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-28T11:49:50Z 2023-05-28T11:49:50Z 2023 Final Year Project (FYP) Phang, R. R. X. (2023). Machine learning / deep learning approach to soundscape evaluations. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167424 https://hdl.handle.net/10356/167424 en A3103-221 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
Phang, Rachel Rei Xuan
Machine learning / deep learning approach to soundscape evaluations
description Masking is the addition of sounds to soundscapes or noise-polluted areas. These additional sounds are known as “maskers”. Soundscape augmentation is a method that involves the addition of “maskers” to a soundscape. It is a noise mitigation method that aims to improve the overall soundscape perception or quality. Many studies have used such techniques to improve the perception of a soundscape. However, the studies conducted have some limitations. The choice of maskers used in those studies are often limited to a single type of masker and are inflexible to real-time soundscapes. The method for selecting maskers also tends to be dependent on experts. This project will be using a machine learning/deep learning approach to select maskers from the given masker database for a soundscape, which can instantaneously and independently predict a suitable masker for that soundscape to create an overall pleasant soundscape.
author2 Gan Woon Seng
author_facet Gan Woon Seng
Phang, Rachel Rei Xuan
format Final Year Project
author Phang, Rachel Rei Xuan
author_sort Phang, Rachel Rei Xuan
title Machine learning / deep learning approach to soundscape evaluations
title_short Machine learning / deep learning approach to soundscape evaluations
title_full Machine learning / deep learning approach to soundscape evaluations
title_fullStr Machine learning / deep learning approach to soundscape evaluations
title_full_unstemmed Machine learning / deep learning approach to soundscape evaluations
title_sort machine learning / deep learning approach to soundscape evaluations
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/167424
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