Date of Award
Dr. Mahdi Abdelguerfi
Dr. Tamjidul Hoque
Dr. Juliet Ioup
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.
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.