Scene understanding based on visual and acoustic data
Convolutional Neural Networks (CNN) is the latest development of neural network. It is a deep learning network having multiple layers of convolution operations to extract image or video features and make predictions. With recent years of rapid development, CNN can be seen everywhere with various app...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/77672 |
| _version_ | 1826112113606656000 |
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| author | Feng, Nijing |
| author2 | Mao Kezhi |
| author_facet | Mao Kezhi Feng, Nijing |
| author_sort | Feng, Nijing |
| collection | NTU |
| description | Convolutional Neural Networks (CNN) is the latest development of neural network. It is a deep learning network having multiple layers of convolution operations to extract image or video features and make predictions. With recent years of rapid development, CNN can be seen everywhere with various applications in real life. New models of CNNs are being developed continuously. In this report, the basic principles of CNN will be discussed, experiments performed will be described, attempts to improve CNN performance with additional acoustic data will be explained. |
| first_indexed | 2024-10-01T03:01:57Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77672 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:01:57Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/776722023-07-07T17:34:10Z Scene understanding based on visual and acoustic data Feng, Nijing Mao Kezhi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Convolutional Neural Networks (CNN) is the latest development of neural network. It is a deep learning network having multiple layers of convolution operations to extract image or video features and make predictions. With recent years of rapid development, CNN can be seen everywhere with various applications in real life. New models of CNNs are being developed continuously. In this report, the basic principles of CNN will be discussed, experiments performed will be described, attempts to improve CNN performance with additional acoustic data will be explained. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-04T02:20:15Z 2019-06-04T02:20:15Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77672 en Nanyang Technological University 67 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Feng, Nijing Scene understanding based on visual and acoustic data |
| title | Scene understanding based on visual and acoustic data |
| title_full | Scene understanding based on visual and acoustic data |
| title_fullStr | Scene understanding based on visual and acoustic data |
| title_full_unstemmed | Scene understanding based on visual and acoustic data |
| title_short | Scene understanding based on visual and acoustic data |
| title_sort | scene understanding based on visual and acoustic data |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/77672 |
| work_keys_str_mv | AT fengnijing sceneunderstandingbasedonvisualandacousticdata |