A domain and noise adversarial bird tune classification pipeline using deep neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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| Jazyk: | English |
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Brac University
2023
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| On-line přístup: | http://hdl.handle.net/10361/21798 |
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10361-217982023-10-15T21:03:12Z A domain and noise adversarial bird tune classification pipeline using deep neural network Riya, Aparna Sarker Roy, Arpita Fahim, Md. Abrar Tasnim, Zarin Islam, Rakibul Mostakim, Moin Reza, Md Tanzim Department of Computer Science and Engineering, Brac University Biodiversity Domain adaptation Classification VGG19 CNN ReLU RESNET50 Machine learning Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-30). Birds are an important category of animals that ecologists keep track of utilizing autonomous recording units as a key indication of environmental health. Because of the consequences of climate change and the rising number of endangered species, many experts suggested developing an animal species recognition system to help them in specialized research. Researchers can improve their ability to assess the state of biodiversity and its patterns in crucial ecosystems by precise sound detection and categorization, which is supported by machine learning, allowing them to better support global conservation efforts. However, producing analysis outputs with high precision and recall remains a difficulty. Due to a lack of appropriate methods for efficient and accurate extraction of interest signals, the vast bulk of data remains unexplored (e.g., bird calls). Moreover, due to strong source-domain specific features and artificial/natural noises, these acquired raw data create different distributions in datasets. So, to ensure a generalized feature learning, domain adaptation [1] techniques will be implemented in this work to make the networks familiar towards both acquisition sensor noises and background noises without having to do intensive dataset specific augmentations. We used 3 popular and powerful DNN models, including CNN, VGG19 and ResNet50. Out of them, for the bird species classification task VGG19 achieved the best accuracy of 96.02% in testing and 94.01% in training. To the best of our knowledge, this will guide towards convenient and deployable in real life models which will allow future works into the pipeline to ensure better coverage. Aparna Sarker Riya Arpita Roy Md. Abrar Fahim Rakibul Islam Zarin Tasnim B.Sc. in Computer Science 2023-10-15T03:44:02Z 2023-10-15T03:44:02Z ©2022 2022-09-29 Thesis ID 18301194 ID 18101332 ID 18301006 ID 18101352 ID 17101478 http://hdl.handle.net/10361/21798 en 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. 41 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Biodiversity Domain adaptation Classification VGG19 CNN ReLU RESNET50 Machine learning Neural networks (Computer science) |
| spellingShingle |
Biodiversity Domain adaptation Classification VGG19 CNN ReLU RESNET50 Machine learning Neural networks (Computer science) Riya, Aparna Sarker Roy, Arpita Fahim, Md. Abrar Tasnim, Zarin Islam, Rakibul A domain and noise adversarial bird tune classification pipeline using deep neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin Riya, Aparna Sarker Roy, Arpita Fahim, Md. Abrar Tasnim, Zarin Islam, Rakibul |
| format |
Thesis |
| author |
Riya, Aparna Sarker Roy, Arpita Fahim, Md. Abrar Tasnim, Zarin Islam, Rakibul |
| author_sort |
Riya, Aparna Sarker |
| title |
A domain and noise adversarial bird tune classification pipeline using deep neural network |
| title_short |
A domain and noise adversarial bird tune classification pipeline using deep neural network |
| title_full |
A domain and noise adversarial bird tune classification pipeline using deep neural network |
| title_fullStr |
A domain and noise adversarial bird tune classification pipeline using deep neural network |
| title_full_unstemmed |
A domain and noise adversarial bird tune classification pipeline using deep neural network |
| title_sort |
domain and noise adversarial bird tune classification pipeline using deep neural network |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21798 |
| work_keys_str_mv |
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