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Previous issue date: 2010-07-23 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The human voice is an important communication tool and any disorder of the
voice can have profound implications for social and professional life of an individual.
Techniques of digital signal processing have been used by acoustic analysis of vocal
disorders caused by pathologies in the larynx, due to its simplicity and noninvasive
nature. This work deals with the acoustic analysis of voice signals affected by
pathologies in the larynx, specifically, edema, and nodules on the vocal folds. The
purpose of this work is to develop a classification system of voices to help pre-diagnosis
of pathologies in the larynx, as well as monitoring pharmacological treatments and after
surgery. Linear Prediction Coefficients (LPC), Mel Frequency cepstral coefficients
(MFCC) and the coefficients obtained through the Wavelet Packet Transform (WPT)
are applied to extract relevant characteristics of the voice signal. For the classification
task is used the Support Vector Machine (SVM), which aims to build optimal
hyperplanes that maximize the margin of separation between the classes involved. The
hyperplane generated is determined by the support vectors, which are subsets of points
in these classes. According to the database used in this work, the results showed a good
performance, with a hit rate of 98.46% for classification of normal and pathological
voices in general, and 98.75% in the classification of diseases together: edema and
nodules / A voz humana ? uma importante ferramenta de comunica??o e qualquer
funcionamento inadequado da voz pode ter profundas implica??es na vida social e
profissional de um indiv?duo. T?cnicas de processamento digital de sinais t?m sido
utilizadas atrav?s da an?lise ac?stica de desordens vocais provocadas por patologias na
laringe, devido ? sua simplicidade e natureza n?o-invasiva. Este trabalho trata da an?lise
ac?stica de sinais de vozes afetadas por patologias na laringe, especificamente, edemas
e n?dulos nas pregas vocais. A proposta deste trabalho ? desenvolver um sistema de
classifica??o de vozes para auxiliar no pr?-diagn?stico de patologias na laringe, bem
como no acompanhamento de tratamentos farmacol?gicos e p?s-cir?rgicos. Os
coeficientes de Predi??o Linear (LPC), Coeficientes Cepstrais de Freq??ncia Mel
(MFCC) e os coeficientes obtidos atrav?s da Transformada Wavelet Packet (WPT) s?o
aplicados para extra??o de caracter?sticas relevantes do sinal de voz. ? utilizada para a
tarefa de classifica??o M?quina de Vetor de Suporte (SVM), a qual tem como objetivo
construir hiperplanos ?timos que maximizem a margem de separa??o entre as classes
envolvidas. O hiperplano gerado ? determinado pelos vetores de suporte, que s?o
subconjuntos de pontos dessas classes. De acordo com o banco de dados utilizado neste
trabalho, os resultados apresentaram um bom desempenho, com taxa de acerto de
98,46% para classifica??o de vozes normais e patol?gicas em geral, e 98,75% na
classifica??o de patologias entre si: edemas e n?dulos
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/15149 |
| Date | 23 July 2010 |
| Creators | Almeida, N?thalee Cavalcanti de |
| Contributors | CPF:70399727434, http://lattes.cnpq.br/6122570451445215, Bresolin, Adriano de Andrade, CPF:66195136972, http://lattes.cnpq.br/8862209760730631, Silveira, Luiz Felipe de Queiroz, CPF:02863206494, http://lattes.cnpq.br/4139452169580807, Albuquerque, Maria Rosa Medeiros Lins de, CPF:08595186472, http://lattes.cnpq.br/4546157125717070, Guerreiro, Ana Maria Guimar?es, Soares, Heliana Bezerra, Silva, Sandro Gon?alves da |
| Publisher | Universidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Engenharia El?trica, UFRN, BR, Automa??o e Sistemas; Engenharia de Computa??o; Telecomunica??es |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Format | application/pdf |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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