• Login
    View Item 
    •   Treasures Home
    • Electronic Theses and Dissertations
    • UTD Theses and Dissertations
    • View Item
    •   Treasures Home
    • Electronic Theses and Dissertations
    • UTD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Real-Time Single and Dual-Channel Speech Enhancement on Edge Devices for Hearing Applications

    Thumbnail
    View/Open
    SHANKAR-DISSERTATION-2021.pdf (37.33Mb)
    Date
    2021-04-26
    Author
    Shankar, Nikhil
    Metadata
    Show full item record
    Abstract
    Abstract
    Speech 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.
    URI
    https://hdl.handle.net/10735.1/9313
    Collections
    • UTD Theses and Dissertations

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of TreasuresCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV