Thesis Defence: Marine Mammal Sounds Classification by Deep Learning

Beiliang Zhao, supervised by Dr. Julian Cheng, will defend their [hesis titled “Marine Mammal Sounds Classification by Deep Learning” in partial fulfillment of the requirements for the degree of Master of Applied Science in Electrical Engineering.

An abstract for Beiliang Zhao’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Please email julian.cheng@ubc.ca to receive the Zoom link for this defence.

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ABSTRACT

Monitoring marine mammals through vocalizations provides scientists with essential insights into their abundance and distribution. Systems that classify these vocalizations enable precise surveillance of specific species, thereby playing a crucial role in the protection efforts for endangered species. However, current solutions based on traditional machine learning technologies face challenges in classifying a diverse range of marine mammal sounds with high accuracy. This study aims to improve classification accuracy for marine mammal vocalizations by refining the feature vectors from sounds, deploying advanced deep-learning techniques, and comparing the performance of various deep-learning models. Furthermore, this research utilizes the generalization capability of deep-learning models to process fresh acoustic data from different sources or additional species with high accuracy. The advancements proposed in this study offer a marked improvement in marine mammal surveillance, allowing for more precise species identification based on their vocal patterns.