Electronic Theses and Dissertations
Date
2025
Document Type
Thesis
Degree Name
Master of Science
Department
Electrical & Computer Engineering
Committee Chair
Eddie Jacobs
Committee Member
Aaron Robinson
Committee Member
Madhusudhanan Balasubramanian
Abstract
The application of neural networks has demonstrated significant potential in the field of acoustic source localization. This thesis investigates the deployment of such advanced neural network models for the specific challenge of uncrewed aerial vehicle (UAV) localization. A persistent obstacle in training deep learning models for this purpose, however, is the considerable effort required to collect and curate sufficiently large real-world datasets, which is both time consuming and resource-intensive. To address this limitation, this research incorporates synthetic data augmentation using a synthetic source sound generation script and the pyroadacoustics simulation environment. These synthetic sources, when processed through the pyroadacoustics simulator, can provide a diverse and physically meaningful set of training examples. This thesis then systematically evaluates whether the integration of such synthetic data into the training process enhances the localization performance of the neural network, potentially reducing the burden of real-world data collection while maintaining, or even improving, model accuracy and generalizability.
Library Comment
Dissertation or thesis originally submitted to ProQuest.
Notes
Open Access
Recommended Citation
Wargo, Noah David, "Effect of Synthetic Data on the Performance of Neural Networks for the Acoustic Localization of Multirotor UAV Targets" (2025). Electronic Theses and Dissertations. 3850.
https://digitalcommons.memphis.edu/etd/3850
Comments
Data is provided by the student.