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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Audio recognition using feed forward neural network optimized by principle component analysis

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    13301061,13301059_CSE.pdf (1.111Mb)
    Date
    4/18/2017
    Publisher
    BRAC University
    Author
    Momo, Nusrat Suzana
    Abdullah
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10361/8240
    Abstract
    In our proposed model we have used PCA as dimension reduction technique and neural network for pattern recognition. Our goal was to recognize audios of two vowels spoken by Parkinson’s disease Patient. The vocal of these patients becomes unclear in later stage of the disease, therefore understanding them becomes difficult and hence our model is targeted to help them communicate. PCA was run to get the finest number of features to train the classifier. The classifier takes 30 percent of the feature to train and the rest 70% for testing and validation. Our model has yield a very high accuracy compared to other models.
    Keywords
    Neural network; Speech recognition
     
    Description
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 26).
     
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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