AI RESEARCH
Optimising MFCC parameters for the automatic detection of respiratory diseases
arXiv CS.LG
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ArXi:2408.07522v2 Announce Type: replace-cross Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) is widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC extraction parameters on respiratory disease diagnosis.