Linear Prediction and Discrete Wavelet Transform to Identify Pathology in Voice Signals
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Data
2017-01-01
Autores
Fonseca, Everthon Silva
Mosconi Pereira, Denis Cesar
Castilho Maschi, Luis Fernando
Guido, Rodrigo Capobianco [UNESP]
Fonseca, Everthon Silva [UNESP]
Silva Paulo, Katia Cristina [UNESP]
IEEE
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee
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
Como citar
2017 Signal Processing Symposium (spsympo). New York: Ieee, 4 p., 2017.