TY - GEN
T1 - Dysphonia Measurements Detection Using CQT’s and MFCC’s Methods
AU - Lopez-Rodríguez, Mario
AU - García-Vázquez, Mireya Sarai
AU - Zamudio-Fuentes, Luis Miguel
AU - Ramírez-Acosta, Alejandro
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Constant Q Transform
KW - Dysphonia measurements
KW - Mel Frequencies Cepstral Coefficients
KW - Parkinson’s disease
KW - Support Vector Machine
KW - Voice analysis
UR - http://www.scopus.com/inward/record.url?scp=85075807046&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30636-6_48
DO - 10.1007/978-3-030-30636-6_48
M3 - Contribución a la conferencia
AN - SCOPUS:85075807046
SN - 9783030306359
T3 - IFMBE Proceedings
SP - 349
EP - 355
BT - Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices - Proceedings of the International Conference on Biomedical and Health Informatics, ICBHI 2019
A2 - Lin, Kang-Ping
A2 - Magjarevic, Ratko
A2 - de Carvalho, Paulo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Biomedical and Health Informatics, ICBHI 2019
Y2 - 17 April 2019 through 20 April 2019
ER -