Efficient Characterization of Cough Sounds Using Statistical Analysis

Cough serves as a principal symptom in respiratory conditions. Variations in cough sound characteristics provide valuable diagnostic insights. There is a lack of evidence in characterizing cough sounds and misinterpretation leads to severe consequences. This paper presents the efficient characterization of cough sounds using statistical analysis; in addition, both cough sound and speech characteristics are compared. The proposed method extracts spectral and time domain attributes, further subjected to statistical and histogram analysis. The results show that the 25th percentile of spectral roll-off and spectral flux, along with the maximum and mean values of spectral flatness, are vital for characterizing cough sounds. Additionally, the maximum and 75th percentile of zero crossing rate, median of spectral bandwidth, and minimum, maximum, median, mean, standard deviation, 25th percentile, and 75th percentile of spectral centroid contribute significantly to this characterization. The distribution of features in cough sounds discloses that spectral roll-off spreads up to 7800 Hz, spectral flatness ranges from 0 to 0.22, spectral flux varies between 0.3 and 0.6, zero crossing rate extends up to 0.4, spectral centroid spans up to 4300 Hz, and spectral bandwidth varies between 1300 Hz to 3200 Hz. Using these attributes as inputs for artificial intelligence models thereby improves respiratory disease diagnosis efficiency.