Recognition of energy-based activities performed by the aged
Over the years, there has been a boom in the mobile technology industry, namely the smartphone industry. With smartphone consumption on the rise, the producers in the mobile industry are compelled to innovate and produce better smartphones with more features than the previous model of smartpho...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149414 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Over the years, there has been a boom in the mobile technology industry, namely the
smartphone industry. With smartphone consumption on the rise, the producers in the mobile
industry are compelled to innovate and produce better smartphones with more features than
the previous model of smartphone. This project aims to utilize the embedded microphones in
our smartphones to recognize home related activities through the unique sound signatures that
these activities produced.
Through some research, it is shown that while speech and music recognition have been
around for some time, the area of environmental sound recognition is relatively unexplored.
However, the basic idea and model composition of environmental sound recognition is
similar. Through this project, Mel Frequency Cepstral Coefficients (MFCCs) will be used for
feature extraction along with the Convolutional Neural Network (CNN) classification model
composition.
The outcome of this project was satisfactory, however there is still room for improvement in
terms of the accuracy. This report includes the research and model process of the project as
well as suggestions for potential future works. |
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