Date of Award
12-2021
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Dr. Mahdi Abdelguerfi
Second Advisor
Dr. Tamjidul Hoque
Third Advisor
Dr. Juliet Ioup
Abstract
Surveys of marine mammal populations are an essential part of monitoring the welfare of these animals and their ecosystems. Marine mammal vocalizations provide a reliable method of identifying most species, but passive acoustic monitoring of underwater audio may generate large quantities of data that exceed the capacity of human classifiers. Preprocessing and machine learning techniques provide a method of automating the classification process. In this study, we explore machine learning approaches to vocalization classification using convolutional neural networks with residual learning. Optimal parameters for noise-removal, spectrographic window functions, preprocessing augmentations, and multi-channel spectrogram generation are derived through a series of tests. Test results inform the construction of a residual network, which we train to high precision. While we demonstrate that multi-channel spectrograms may provide additional acoustic information, we find that single-channel spectrograms offer superior classification performance in most cases.
Recommended Citation
Murphy, Daniel T., "Analysis of Residual Neural Networks for Marine Mammal Classification using Multi-channel Spectrograms" (2021). University of New Orleans Theses and Dissertations. 2936.
https://scholarworks.uno.edu/td/2936
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.