Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/257113
Title: Acoustic Analysis for Human Voice Disorder Classification Using Optimization and Machine Learning Techniques
Researcher: Sheela Selvakumari N A
Guide(s): Radha V
Keywords: Voice Disorder, Speech Processing, MFCC, Cat Swarm Optimization, SVM and BPNN
University: Avinashilingam Deemed University For Women
Completed Date: 16.08.2019
Abstract: The diagnosis of voice disorders through aggressive medical techniques seems painful for patients. Hence, automatic speech recognition and disorder identification methods have drawn much interest in the recent years and have proved to be successful. In this work, voice recordings are taken from the Saarbruecken Voice Database. The signals are preprocessed to remove silence and de-noised using Hybrid Wiener Filter Discrete Wavelet Transforms (HWFDWT). Features are extracted using Cat Swarm Optimization Mel Frequency Cepstrum Coefficients (CSOMFCC). Finally, the features are classified using Classification using Modified Optimized Back Propagation Network Disorder voice Classification (MOBPNDC). The classification scheme outperforms the existing Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) methods in terms of Accuracy, Precision, Recall, F-Measure and Time period. newlinei) Major objectives : newlineTo develop an automatic voice pathological identification system based on vocal parameters to make the decision about the speech sample as Pathological or Normal using various speech features and machine learning classifiers. newlineiv) Findings: newlineand#61623; Proposed the Hybrid Wiener Filter Discrete Wavelet Transforms (HWFDWT) for Silence Removal and Noise Removal in Pre-processing techniques. newlineand#61623; Proposed the Wavelet Thresholding Algorithm for Silence Removal and Noise Removal. newlineand#61623; Proposed Cat Swarm Optimization Mel Frequency Cepstrum Coefficients (CSOMFCC) to extract the best features from the signal in the Feature Extraction process. newlineand#61623; Proposed the Modified Optimized Back Propagation Network Disorder Voice Classification (MOBPNDVC) for Voice Pathology Identification System construction. newlineand#61623; Proposed MOBPNDVC Optimization Algorithm for Disorder Voice classification accuracy calculation. newlineand#61623; Analyzed the Voice Pathological Identification System to identify five types of voice pathology such as Laryngitis, Laryngoceles, Dysphonia, Diplophonia, and Chorditis
Pagination: 167 p.
URI: http://hdl.handle.net/10603/257113
Appears in Departments:Department of Computer Science

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File103.26 kBAdobe PDFView/Open
02_certificate.pdf396.47 kBAdobe PDFView/Open
03_acknowledgement.pdf112.42 kBAdobe PDFView/Open
04_contents.pdf207.18 kBAdobe PDFView/Open
05_list of tables, figures and abbreviations.pdf105.96 kBAdobe PDFView/Open
06_chapter 1.pdf6.57 MBAdobe PDFView/Open
07_chapter 2.pdf6.68 MBAdobe PDFView/Open
08_chapter 3.pdf6.5 MBAdobe PDFView/Open
09_chapter 4.pdf6.74 MBAdobe PDFView/Open
10_chapter 5.pdf6.71 MBAdobe PDFView/Open
11-chapter 6.pdf6.46 MBAdobe PDFView/Open
12_bibliography.pdf6.55 MBAdobe PDFView/Open
13_publications.pdf6.38 MBAdobe PDFView/Open
Show full item record


Items in Shodhganga are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric Badge: