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 |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/139490 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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%. |
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