Sound Source Localization for Improving Hearing Aid Studies Using Mobile Platforms

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dc.contributor.advisorPanahi, Issa M.S.
dc.contributor.advisorNtafos, Simeon
dc.contributor.committeeMemberBusso, Carlos
dc.contributor.committeeMemberNourani, Mehrdad
dc.contributor.committeeMemberNosratinia, Aria
dc.creatorKucuk, Abdullah
dc.date.accessioned2024-03-22T19:34:20Z
dc.date.available2024-03-22T19:34:20Z
dc.date.created2021-12
dc.date.issuedDecember 2021
dc.date.submittedDecember 2021
dc.date.updated2024-03-22T19:34:21Z
dc.description.abstractMicrophone array is one of the powerful techniques that enables to apply effective signal processing algorithms to systems. One of the critical application areas of microphone array is sound source localization (SSL), which refers to identify the speaker of interest using a microphone array. SSL can be used as a preprocessing technique to boost up the entire system efficiency. Recent studies show that smartphones can be an efficient assistive device for hearing aid devices because of smartphones’ powerful hardware and software components. Also, Deep Learning (DL) has shown a considerable performance increase in audio signal processing. DL based SSL using the direction of arrival estimation (DOA) methods for two and eight microphone array structures and the distance estimation methods using a single microphone are proposed in this work. The performance of the proposed methods are evaluated in several realistic noisy conditions, reverberations using real-recorded data. Another contribution of this work is to present real-time implementations of the DL based methods on edges devices, i.e., smartphones, tablets.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/10082
dc.language.isoen
dc.subjectEngineering, Electronics and Electrical
dc.titleSound Source Localization for Improving Hearing Aid Studies Using Mobile Platforms
dc.typeThesis
dc.type.materialtext
local.embargo.lift2023-12-01
local.embargo.terms2023-12-01
thesis.degree.collegeSchool of Engineering and Computer Science
thesis.degree.departmentElectrical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.namePHD

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