Energy related activities recognition using smartphones

In recent years, there is increased ownership and reliance on the smartphone. The increased demand and competition from other market competitor has motivated smartphone producer to include more hardware and functions into the smartphone produced. This project explored the possibility of using microp...

Full description

Saved in:
Bibliographic Details
Main Author: Lim, Sean Dao Chuan
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/140381
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-140381
record_format dspace
spelling sg-ntu-dr.10356-1403812023-07-07T18:51:52Z Energy related activities recognition using smartphones Lim, Sean Dao Chuan Soh Yeng Chai School of Electrical and Electronic Engineering EYCSOH@ntu.edu.sg Engineering::Electrical and electronic engineering In recent years, there is increased ownership and reliance on the smartphone. The increased demand and competition from other market competitor has motivated smartphone producer to include more hardware and functions into the smartphone produced. This project explored the possibility of using microphone embedded within the smartphone to recognise home-related activities through the unique audio signature produced. Research done has shown that while sound recognition, specifically music and speech recognition, has been around for quite some time, the area of environmental sound recognition is relatively untapped. However, the basic methodology and idea to develop a classification model are similar. For this project, Mel-Frequency Cepstral Coefficients (MFCCs) are used for feature extraction and Convolutional Neural Network (CNN) is used as the preferred classification model. The outcome of this project was satisfactory, yet there is still room for improvements to the system in terms of accuracy as well as performance. This report will cover the research and development process of the project as well as suggestions for potential future development. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-28T08:12:50Z 2020-05-28T08:12:50Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140381 en A1159-191 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
Lim, Sean Dao Chuan
Energy related activities recognition using smartphones
description In recent years, there is increased ownership and reliance on the smartphone. The increased demand and competition from other market competitor has motivated smartphone producer to include more hardware and functions into the smartphone produced. This project explored the possibility of using microphone embedded within the smartphone to recognise home-related activities through the unique audio signature produced. Research done has shown that while sound recognition, specifically music and speech recognition, has been around for quite some time, the area of environmental sound recognition is relatively untapped. However, the basic methodology and idea to develop a classification model are similar. For this project, Mel-Frequency Cepstral Coefficients (MFCCs) are used for feature extraction and Convolutional Neural Network (CNN) is used as the preferred classification model. The outcome of this project was satisfactory, yet there is still room for improvements to the system in terms of accuracy as well as performance. This report will cover the research and development process of the project as well as suggestions for potential future development.
author2 Soh Yeng Chai
author_facet Soh Yeng Chai
Lim, Sean Dao Chuan
format Final Year Project
author Lim, Sean Dao Chuan
author_sort Lim, Sean Dao Chuan
title Energy related activities recognition using smartphones
title_short Energy related activities recognition using smartphones
title_full Energy related activities recognition using smartphones
title_fullStr Energy related activities recognition using smartphones
title_full_unstemmed Energy related activities recognition using smartphones
title_sort energy related activities recognition using smartphones
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/140381
_version_ 1772826472379056128