Theses and Dissertations

ORCID

https://orcid.org/0009-0005-6778-1467

Advisor

Burch, Reuben F., V.

Committee Member

Strawderman, Lesley

Committee Member

Ball, John E.

Committee Member

Saucier, David

Date of Degree

5-10-2024

Original embargo terms

Immediate Worldwide Access

Document Type

Graduate Thesis - Open Access

Major

Industrial and Systems Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

Misophonia is a sensory disorder where specific stimuli, usually auditory, trigger the fight-flight-freeze response, causing extreme reactions, typically anger, panic, or anxiety. Research into treatment for misophonia is limited, primarily consisting of case studies applying common methods of therapy. However, research into similar disorders like tinnitus shows that there are many avenues of treatment that should be investigated, including audiological treatment. To apply audiological treatment to misophonia, selective noise cancelling must be used to control specific trigger sounds. In this research, a basic selective noise cancelling algorithm was developed using a convolutional neural network and was evaluated using a survey. Participants rated their reaction to trigger sounds, non-trigger sounds, and trigger sounds that had been selectively cancelled. The misophonic reactions to selectively cancelled sounds were significantly less than to trigger sounds. This shows that selective noise cancelling could be used to apply audiological treatments to misophonia.

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