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dc.contributor.advisorUddin, Dr. Jia
dc.contributor.authorMomo, Nusrat Suzana
dc.contributor.authorAbdullah
dc.date.accessioned2017-06-07T08:08:15Z
dc.date.available2017-06-07T08:08:15Z
dc.date.copyright2017
dc.date.issued4/18/2017
dc.identifier.otherID 13301059
dc.identifier.otherID 13301061
dc.identifier.urihttp://hdl.handle.net/10361/8240
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 26).
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.description.abstractIn 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.en_US
dc.description.statementofresponsibilityNusrat Suzana Momo
dc.description.statementofresponsibilityAbdullah
dc.format.extent26 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectNeural networken_US
dc.subjectSpeech recognitionen_US
dc.titleAudio recognition using feed forward neural network optimized by principle component analysisen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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