scholarly journals The Use of LPC and Wavelet Transform for Influenza Disease Modeling

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 590 ◽  
Author(s):  
Khaled Daqrouq ◽  
Mohammed Ajour
Keyword(s):  
Wavelet Transform ◽  
Linear Prediction ◽  
Disease Modeling ◽  
New Combination ◽  
Discrete Wavelet ◽  
Pathological System ◽  
Verification System ◽  
Recognition Systems ◽  
Linear Prediction Coding ◽  
Very High

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.

Linear prediction and discrete wavelet transform to identify pathology in voice signals

2017 ◽  
Author(s):  
Everthon Silva Fonseca ◽  
Denis Cesar Mosconi Pereira ◽  
Luis Fernando Castilho Maschi ◽  
Rodrigo Capobianco Guido ◽  
Everthon Silva Fonseca ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Linear Prediction ◽  
Discrete Wavelet

scholarly journals The Use of Arabic Vowels to Model the Pathological Effect of Influenza Disease by Wavelets

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Khaled Daqrouq ◽  
Abdel-Rahman Al-Qawasmi ◽  
Ahmed Balamesh ◽  
Ali S. Alghamdi ◽  
Mohamed A. Al-Amoudi
Keyword(s):  
Linear Prediction ◽  
Nearest Neighbor ◽  
Vocal Tract ◽  
Characteristic Curve ◽  
Voice Disorder ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Pathological Effect ◽  
Power Spectral ◽  
Linear Prediction Coding

Speech parameters may include perturbation measurements, spectral and cepstral modeling, and pathological effects of some diseases, like influenza, that affect the vocal tract. The verification task is a very good process to discriminate between different types of voice disorder. This study investigated the modeling of influenza’s pathological effects on the speech signals of the Arabic vowels “A” and “O.” For feature extraction, linear prediction coding (LPC) of discrete wavelet transform (DWT) subsignals denoted by LPCW was used. k-Nearest neighbor (KNN) and support vector machine (SVM) classifiers were used for classification. To study the pathological effects of influenza on the vowel “A” and vowel “O,” power spectral density (PSD) and spectrogram were illustrated, where the PSD of “A” and “O” was repressed as a result of the pathological effects. The obtained results showed that the verification parameters achieved for the vowel “A” were better than those for vowel “O” for both KNN and SVM for an average. The receiver operating characteristic curve was used for interpretation. The modeling by the speech utterances as words was also investigated. We can claim that the speech utterances as words could model the influenza disease with a good quality of the verification parameters with slightly less performance than the vowels “A” as speech utterances. A comparison with state-of-the-art method was made. The best results were achieved by the LPCW method.

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