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Researcher: Safdar Tanweer
Guide(s): Abdul Mobin
Keywords: Artificial Neural Network; Dynamic Time Warping; Voice activation detection.
University: Jamia Hamdard University
Completed Date: 2017
Abstract: The aim is to recognize, compare, newlineclassify and identify with the help of computation and applied techniques. To perform newlinethe task of classification of noise sources, LPC and MFCC were used as input to the newlineclassifiers in experimental work. LDA, QDA and ANN are tested for the classification newlinepurpose. Once the source is identified we can address these untoward noisiness class newlineand to minimize their impact to the human perceptions through the implementation of newlineappropriate technique/devices to enhance the system recognition efficiency. The newlineperformance of LDA, QDA and ANN with LPC and MFCC is analyzed. It is evident that newlineANN in combination with MFCC gives the best result and showing efficiency about newline90%. newline
Appears in Departments:Department of Computer Science

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certificate.pdfAttached File393.47 kBAdobe PDFView/Open
chapter-1.pdf585.5 kBAdobe PDFView/Open
chapter-2.pdf292.17 kBAdobe PDFView/Open
chapter-3.pdf1.04 MBAdobe PDFView/Open
chapter-4.pdf474.71 kBAdobe PDFView/Open
chapter-5.pdf834.95 kBAdobe PDFView/Open
chapter-6.pdf1.19 MBAdobe PDFView/Open
contents.pdf441.14 kBAdobe PDFView/Open
list of figures & tables.pdf624.38 kBAdobe PDFView/Open
preface.pdf7.91 kBAdobe PDFView/Open
references.pdf522.86 kBAdobe PDFView/Open
title.pdf195.03 kBAdobe PDFView/Open

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