Highly efficient sound classification for marine mammals
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Liu, Xiangrui
issued: 2024-01-11T22:22:08Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Marine mammals and their ecosystem face significant threats from, for example, military active sonar and marine transportation. To mitigate this harm, early detection and classification of marine mammals are essential. Recent solutions involve spectrogram comparison and machine learning. However, the solutions show weaknesses in efficiency. Therefore, we propose a novel knowledge distillation framework, named XCFSMN, for this problem. We construct a teacher model that fuses the features extracted from an X-vector extractor, a DenseNet, and a Cross-Covariance attended compact Feed-Forward Sequential Memory Network (cFSMN). The teacher model transfers knowledge to a simpler cFSMN model through a temperature-cooling strategy for efficient learning. Compared to multiple convolutional neural network backbones and transformers, the proposed framework achieves state-of-the-art efficiency and performance. The improved model size is approximately 25 times smaller and the inference time is 27 times shorter on average without affecting the model’s accuracy significantly.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87221
note: Highly Efficient Sound Classificationfor Marine MammalsbyXiangrui LiuB.Sc., The University of British Columbia, 2020A 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)January 2024© Xiangrui Liu 2024The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, the thesis entitled:Highly Efficient Sound Classification for Marine Mammals submitted byXiangrui Liu in partial fulfillment of the requirements forthe degree of Master of Applied Science.Dr. Julian Cheng, School of EngineeringSupervisorDr. Shan Du, Irving K Barber Faculty of ScienceCo-supervisorDr. Jahangir Hossain, School of EngineeringSupervisory Committee MemberDr. Thomas Johnson, School of EngineeringSupervisory Committee MemberDr. Mahmudur Fatmi, School of EngineeringExternal ExamineriiAbstractMarine mammals and their ecosystem face significant threats from, forexample, military active sonar and marine transportation. To mitigatethis harm, early detection and classification of marine mammals are essen-tial. Recent solutions involve spectrogram comparison and machine learn-ing. However, the solutions show weaknesses in efficiency. Therefore, wepropose a novel knowledge distillation framework, named XCFSMN, forthis problem. We construct a teacher model that fuses the features ex-tracted from an X-vector extractor, a DenseNet, and a Cross-Covariance at-tended compact Feed-Forward Sequential Memory Network (cFSMN). Theteacher model transfers knowledge to a simpler cFSMN model through atemperature-cooling strategy for efficient learning. Compared to multipleconvolutional neural network backbones and transformers, the proposedframework achieves state-of-the-art efficiency and performance. The im-proved model size is approximately 25 times smaller and the inference timeis 27 times shorter on average withou…
has type: Thesis/Dissertation
date issued: 2024-02
is shown at: 10.14288/1.0438632
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
contributor: Du, Shan
type: Text
identifier URI: http://hdl.handle.net/2429/87221