Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC

TL;DR

This study analyzes COVID-19 related sounds using MFCC features and correlation analysis, revealing high similarity in cough and breathing sounds among COVID-19 patients, which could aid in diagnosis.

Contribution

It introduces a correlation-based analysis of MFCC features to distinguish COVID-19 sounds from non-COVID sounds, highlighting potential diagnostic applications.

Findings

High MFCC similarity in COVID-19 coughs and breaths

MFCC of voice more robust across COVID-19 and non-COVID-19 samples

Preliminary results suggest voice exclusion could aid diagnosis

Abstract

Recently there has been a formidable work which has been put up from the people who are working in the frontlines such as hospitals, clinics, and labs alongside researchers and scientists who are also putting tremendous efforts in the fight against COVID-19 pandemic. Due to the preposterous spread of the virus, the integration of the artificial intelligence has taken a considerable part in the health sector, by implementing the fundamentals of Automatic Speech Recognition (ASR) and deep learning algorithms. In this paper, we illustrate the importance of speech signal processing in the extraction of the Mel-Frequency Cepstral Coefficients (MFCCs) of the COVID-19 and non-COVID-19 samples and find their relationship using Pearson correlation coefficients. Our results show high similarity in MFCCs between different COVID-19 cough and breathing sounds, while MFCC of voice is more robust between COVID-19 and non-COVID-19 samples. Moreover, our results are preliminary, and there is a possibility to exclude the voices of COVID-19 patients from further processing in diagnosing the disease.