Sound based snore signal analysis using ELM
Snoring is a common symptom faced by many people. Its features can be analysed to provide insight or help in the diagnosis of sleep related disorders. This project aims to use feature extraction and a learning machine algorithm to predict the presence of snores in a given sound sample.
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2012
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sg-ntu-dr.10356-502672023-07-07T16:40:09Z Sound based snore signal analysis using ELM Toh, Violet Mei Sze. Ser Wee School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Snoring is a common symptom faced by many people. Its features can be analysed to provide insight or help in the diagnosis of sleep related disorders. This project aims to use feature extraction and a learning machine algorithm to predict the presence of snores in a given sound sample. Bachelor of Engineering 2012-05-31T04:35:10Z 2012-05-31T04:35:10Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/50267 en Nanyang Technological University 73 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Toh, Violet Mei Sze. Sound based snore signal analysis using ELM |
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Snoring is a common symptom faced by many people. Its features can be analysed to provide insight or help in the diagnosis of sleep related disorders. This project aims to use feature extraction and a learning machine algorithm to predict the presence of snores in a given sound sample. |
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Ser Wee |
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Ser Wee Toh, Violet Mei Sze. |
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Final Year Project |
author |
Toh, Violet Mei Sze. |
author_sort |
Toh, Violet Mei Sze. |
title |
Sound based snore signal analysis using ELM |
title_short |
Sound based snore signal analysis using ELM |
title_full |
Sound based snore signal analysis using ELM |
title_fullStr |
Sound based snore signal analysis using ELM |
title_full_unstemmed |
Sound based snore signal analysis using ELM |
title_sort |
sound based snore signal analysis using elm |
publishDate |
2012 |
url |
http://hdl.handle.net/10356/50267 |
_version_ |
1772828597730410496 |