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Open AccessArticle

The Use of LPC and Wavelet Transform for Influenza Disease Modeling

Department of Electrical and Computer Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
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Entropy 2018, 20(8), 590; https://doi.org/10.3390/e20080590
Received: 3 March 2018 / Revised: 21 May 2018 / Accepted: 2 June 2018 / Published: 9 August 2018
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory III)
In this paper, we investigated the modeling of the pathological features of the influenza disease on the human speech. The presented work is novel research based on a real database and a new combination of previously used methods, discrete wavelet transform (DWT) and linear prediction coding (LPC). Three verification system experiments, Normal/Influenza, Smokers/Influenza, and Normal/Smokers, were studied. For testing the proposed pathological system, several classification scores were calculated for the recorded database, from which we can see that the proposed method achieved very high scores, particularly for the Normal with Influenza verification system. The performance of the proposed system was also compared with other published recognition systems. The experiments of these schemes show that the proposed method is superior. View Full-Text
Keywords: LPC; wavelet transform; speech; influenza disease modeling LPC; wavelet transform; speech; influenza disease modeling
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Daqrouq, K.; Ajour, M. The Use of LPC and Wavelet Transform for Influenza Disease Modeling. Entropy 2018, 20, 590.

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