RansomListener: Ransom call sound investigation using LSTM and CNN Architectures
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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Brac University
2021
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| Acesso em linha: | http://hdl.handle.net/10361/14804 |
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10361-148042022-01-26T10:13:13Z RansomListener: Ransom call sound investigation using LSTM and CNN Architectures Rahman, Rafeed Rahman, Mehfuz A Hossain, Shahriar Hossain, Sajid Milon, Md.Iqbal Hossain Akhond, Mostafijur Rahman Department of Computer Science and Engineering, Brac University Convolution AlexNET VGG16 LSTM Neural Network This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 27-30). Getting calls for ransoms are common phenomena in kidnapping and abduction related incidents where the life of the victim remains extremely vulnerable. These phone calls are often analyzed in real-time by law enforcement authorities to quickly identify the suspects and get crucial information for quick action. However, it is often difficult to manually analyze those phone calls due to the quality of sounds and the presence of several background noises. Even with much high-end software in their inventory, it is futile to accurately refine the incoming calls as it takes a huge amount of time to declutter the different layers of noises in the call. This paper proposes a model based on deep convolutional neural network and signal processing for automatic classification of crucial sounds in ransom related phone calls. We have proposed LSTM and 2D CNN customized models and compared their outputs with VGG16 and AlexNet. Moreover, this paper also presents a unique dataset of different sounds in terms of voices like male or female and the environmental sounds where the victim might be in which can be a probable clue for investigation purposes consisting of 17650 audio clips collected from verified online sources. Finally, the models produced very high classification accuracy with the accuracy of LSTM reaching around 93.4%. Rafeed Rahman Mehfuz A Rahman Shahriar Hossain Sajid Hossain B. Computer Science 2021-07-15T04:20:45Z 2021-07-15T04:20:45Z 2020 2020-12 Thesis ID: 17101502 ID: 17101378 ID: 17101370 ID: 17101352 http://hdl.handle.net/10361/14804 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 30 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Convolution AlexNET VGG16 LSTM Neural Network |
| spellingShingle |
Convolution AlexNET VGG16 LSTM Neural Network Rahman, Rafeed Rahman, Mehfuz A Hossain, Shahriar Hossain, Sajid RansomListener: Ransom call sound investigation using LSTM and CNN Architectures |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Milon, Md.Iqbal Hossain |
| author_facet |
Milon, Md.Iqbal Hossain Rahman, Rafeed Rahman, Mehfuz A Hossain, Shahriar Hossain, Sajid |
| format |
Thesis |
| author |
Rahman, Rafeed Rahman, Mehfuz A Hossain, Shahriar Hossain, Sajid |
| author_sort |
Rahman, Rafeed |
| title |
RansomListener: Ransom call sound investigation using LSTM and CNN Architectures |
| title_short |
RansomListener: Ransom call sound investigation using LSTM and CNN Architectures |
| title_full |
RansomListener: Ransom call sound investigation using LSTM and CNN Architectures |
| title_fullStr |
RansomListener: Ransom call sound investigation using LSTM and CNN Architectures |
| title_full_unstemmed |
RansomListener: Ransom call sound investigation using LSTM and CNN Architectures |
| title_sort |
ransomlistener: ransom call sound investigation using lstm and cnn architectures |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14804 |
| work_keys_str_mv |
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| _version_ |
1814307996990177280 |