Cepstral Coefficient Extraction using the MFCC with the Discrete Wavelet Transform for the Parkinson's Disease Diagnosis

Cepstral Coefficient Extraction using the MFCC with the Discrete Wavelet Transform for the Parkinson's Disease Diagnosis
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Belhoussine Drissi Taoufiq, Zayrit Soumaya, Nsiri Benayad, Boualoulou Nouhaila
DOI : 10.14445/22315381/IJETT-V70I7P229

How to Cite?

Belhoussine Drissi Taoufiq, Zayrit Soumaya, Nsiri Benayad, Boualoulou Nouhaila, "Cepstral Coefficient Extraction using the MFCC with the Discrete Wavelet Transform for the Parkinson's Disease Diagnosis" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 283-290, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P229

Abstract
Studies consider Parkinson's disease the second most common neurological syndrome after Alzheimer's. To get an accurate diagnosis, we have recourse to the signal treatment. One of the most used methods in sound recognition and speaker identification is the Mel Frequency Cepstral Coefficients (MFCC). In this technique, the first step is Pre-emphasize the signal, then frame and window it. After that, the Cepstral analysis in which the Fast Fourier Transform (FFT) is applied, followed by the Discrete Cosine Transform (DCT) and the liftering of the Mel Frequency Cepstral Coefficients. This paper proposes to work with the Discrete Wavelet Transform (DWT) instead of the filter bank at the filtering step. Using data containing 17 healthy and 20 patients with Parkinson's, 12 MFCC coefficients will be extracted and classified using the Support Vector Machine (SVM). Thence, a method consisting of inserting DWT into the MFCC bloc will be conducted to compare the result with the previous experience when DWT was outside the MFCC bloc and with the one using only the MFCC without DWT to fetch the accurate process of Parkinson's disease detection.

Keywords
Parkinson's disease, MFCC, Daubechies, Wavelet, Filter bank, SVM.

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