Marine mammal sound classification using deep learning models
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Zhao, Beiliang
issued: 2024-01-08T18:43:58Z
issued: 2023
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Monitoring marine mammals through vocalizations provides scientists with essential insights into their abundance and distribution. Systems that classify these acoustic sensing from less than 10 to over 100000 hertz (Hz) 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 models, 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.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87147
note: Marine Mammal Sound ClassificationUsing Deep Learning ModelsbyBeiliang ZhaoB.Eng., Beijing Jiaotong University, 2021B.Eng., University of Wollongong, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)December 2023© Beiliang Zhao, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Marine Mammal Sound Classification Using Deep LearningModelssubmitted by Beiliang Zhao in partial fulfilment of the requirements ofthe degree of Master of Applied ScienceJulian Cheng, School of EngineeringSupervisorThomas Johnson, School of EngineeringSupervisory Committee MemberAnas Chaaban, School of EngineeringSupervisory Committee MemberQian Chen, School of EngineeringUniversity ExamineriiAbstractMonitoring marine mammals through vocalizations provides scientistswith essential insights into their abundance and distribution. Systems thatclassify these acoustic sensing from less than 10 to over 100000 hertz (Hz)enable precise surveillance of specific species, thereby playing a crucial rolein the protection efforts for endangered species. However, current solutionsbased on traditional machine learning technologies face challenges in classi-fying a diverse range of marine mammal sounds with high accuracy. Thisstudy aims to improve classification accuracy for marine mammal vocaliza-tions by refining the feature vectors from sounds, deploying advanced deep-learning models, and comparing the performance of various deep-learningmodels. Furthermore, this research utilizes the generalization capability ofdeep-learning models to process fresh acoustic data from different sources oradditional species with high accuracy. The advancements proposed in thisstudy offer a marked improvement in marine mammal surveillance, allowingfor more precise s…
has type: Thesis/Dissertation
date issued: 2024-02
is shown at: 10.14288/1.0438572
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Cheng, Julian
type: Text
identifier URI: http://hdl.handle.net/2429/87147