Publicação: Linear Prediction and Discrete Wavelet Transform to Identify Pathology in Voice Signals
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Data
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Coorientador
Pós-graduação
Curso de graduação
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ISSN da Revista
Título de Volume
Editor
Ieee
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
Resumo
This work describes an algorithm to help in the identification of pathologically affected voices. Based on inverse linear prediction filter (LPC) and discrete wavelet transform (DWT), this method can be used in conjunction with other classifiers in order to improve them, by the addition of the new parameter we propose, DWT-RMS. Using no association with other methods, DWT-RMS gives quantitative evaluation of voice signals from male and female subjects of different ages and leads to an adequate larynx pathology classifier with 85.94% of classification accuracy, 0% of false negatives and 14.06% of false positives, to identify nodules in vocal folds.
Descrição
Palavras-chave
prediction, wavelet, pathologies, voice, signals
Idioma
Inglês
Como citar
2017 Signal Processing Symposium (spsympo). New York: Ieee, 4 p., 2017.
