Date of Award

1-29-2024

Publication Type

Thesis

Degree Name

M.A.Sc.

Department

Electrical and Computer Engineering

Keywords

Sub-node Neural Network;Voice Pathology Detection

Supervisor

Jonathan Wu

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The human voice carries unique individual characteristics and finds widespread application in voice pathology detection (VPD). In this thesis, human audio recordings are utilized to develop a readily accessible, rapid, and efficient methodology for the automated detection of Coronavirus 2019 (COVID-19). This endeavour represents a solution to the challenge of COVID-19 and its variants detection. The paper proposes a novel one class classification model based on the sub-node neural network (OCC-SNN). Unlike conventional approaches that incorporate multiple layers in the hidden space and employ complex techniques to determine the global optimum, the proposed network is clearer and more interpretable. In each iteration, one sub-node is added to the original structure, and the weights of the new sub-node are calculated using the Moore-Penrose (MP) inverse and the error obtained from the previous iteration. Furthermore, a detailed method of equalizing Mel-frequency cepstral coefficients (MFCCs) is proposed to extract the features from audio recordings. Furthermore, utilizing the original Coswara dataset, we manually curated optimized datasets to facilitate a more comprehensive evaluation. The proposed COVID-19 detection frame approach is able to distinguish positive sample from healthy samples, and the proposed OCC-SNN algorithm also extends to effectively image-based anomaly detection.

Available for download on Saturday, February 01, 2025

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