Empirical tuning of MFCC parameters (roughly 30 coefficients, shorter hops, dataset-dependent frame lengths) improves SVM accuracy for respiratory disease detection by 14.9-19.6% on COVID-19 and voice-disorder datasets.
Voice pathology detection on the saarbr ¨ucken voice database with calibration and fusion of scores using multifocal toolkit
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Optimising MFCC parameters for the automatic detection of respiratory diseases
Empirical tuning of MFCC parameters (roughly 30 coefficients, shorter hops, dataset-dependent frame lengths) improves SVM accuracy for respiratory disease detection by 14.9-19.6% on COVID-19 and voice-disorder datasets.