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2020 | OriginalPaper | Chapter

Dysphonia Measurements Detection Using CQT’s and MFCC’s Methods

Authors : Mario Lopez-Rodríguez, Mireya Sarai García-Vázquez, Luis Miguel Zamudio-Fuentes, Alejandro Ramírez-Acosta

Published in: Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices

Publisher: Springer International Publishing

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Abstract

Dysphonia is a vocal impediment that appears as a symptom of Parkinson’s disease, and can be used for its diagnosis. Among the important measurements for dysphonia detection are jitter, shimmer, fundamental frequency (F0), Harmonics to noise ratio (HNR) and noise to harmonics ratio (NHR). The frequency space of the speech signal is used to detect these five dysphonia measurements, through this space the acoustic markers jitter, shimmer and F0 are calculated. In this article, an evaluation of the detection of acoustic markers is presented through the mathematical methods of the Constant Q Transform (CQT) and the Mel Frequencies Cepstral Coefficients (MFCC) in speech signals of patients with Parkinson’s disease. The classifier method Support Vector Machine (SVM) is used to detect the Biomarkers. According to the results, the CQT method and MFCC method (57% and 62% precision respectively) which is a promising results for Parkinson’s disease diagnosis by the detection of Dysphonia measurements.

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Metadata
Title
Dysphonia Measurements Detection Using CQT’s and MFCC’s Methods
Authors
Mario Lopez-Rodríguez
Mireya Sarai García-Vázquez
Luis Miguel Zamudio-Fuentes
Alejandro Ramírez-Acosta
Copyright Year
2020
Book
Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices

Print ISBN: 978-3-030-30635-9

Electronic ISBN: 978-3-030-30636-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-30636-6_48