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dc.contributor.advisorPanahi, Issa M.S.
dc.creatorShankar, Nikhil
dc.date.accessioned2021-12-09T14:20:33Z
dc.date.available2021-12-09T14:20:33Z
dc.date.created2021-05
dc.date.issued2021-04-26
dc.date.submittedMay 2021
dc.identifier.urihttps://hdl.handle.net/10735.1/9313
dc.description.abstractSpeech Enhancement (SE) is an important module in the signal processing pipeline for hearing applications and it helps enhance the comfort of listening. Many single and dualmicrophone SE techniques have been developed by researchers over the last few decades. In this thesis, novel single and dual-channel SE techniques have been proposed and are implemented on edge devices as an assistive tool for hearing applications. The smartphone is considered as the processing platform for real-time implementation and testing. In this work, both statistical signal processing and deep learning algorithms are proposed for SE. Firstly, we compare different two-channel beamformers for SE. Later, the Minimum Variance Distortionless Response (MVDR) beamformer assisted by a voice activity detector (VAD) is used as a Signal to Noise Ratio (SNR) booster for the SE method. Deep neural network architectures comprising of convolutional neural network (CNN) and recurrent neural network (RNN) layers are proposed in this thesis for real-time SE. Finally to filter out background noise, the SE gain estimation for noisy speech mixture is smoothed along the frequency axis by a Mel filter-bank, resulting in a Mel-warped frequency-domain gain estimation. In comparison with existing SE methods, objective assessment and subjective results of the developed methods indicate substantial improvements in speech quality and intelligibility.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectHearing aids
dc.subjectSmartphones
dc.subjectSpeech perception
dc.subjectSpeech processing systems
dc.titleReal-Time Single and Dual-Channel Speech Enhancement on Edge Devices for Hearing Applications
dc.typeThesis
dc.date.updated2021-12-09T14:20:34Z
dc.type.materialtext
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.departmentElectrical Engineering
thesis.degree.levelDoctoral
thesis.degree.namePHD


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