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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/139490 |
| _version_ | 1826116713433792512 |
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| author | Chua, MingHui |
| author2 | Gan Woon Seng |
| author_facet | Gan Woon Seng Chua, MingHui |
| author_sort | Chua, MingHui |
| collection | NTU |
| 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%. |
| first_indexed | 2024-10-01T04:15:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/139490 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:15:53Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering::Electrical and electronic engineering Chua, MingHui Urban noise classification for active noise control in residential buildings |
| title | 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_short | Urban noise classification for active noise control in residential buildings |
| title_sort | urban noise classification for active noise control in residential buildings |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/139490 |
| work_keys_str_mv | AT chuaminghui urbannoiseclassificationforactivenoisecontrolinresidentialbuildings |