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

Alsabek, Mohammed Bader; Shahin, Ismail; Hassan, Abdelfatah

doi:10.1109/ccci49893.2020.9256700

Cited by 43 publications

(22 citation statements)

References 10 publications

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“…Schuller et al (2020) studied what computer audition could possibly contribute to the ongoing battle against the COVID-19. Other recent acoustic analyses for the detection of COVID-19 can be found in Deshpande and Schuller (2020), Alsabek et al (2020), Pal andSankarasubbu (2020), Quatieri et al (2020), Laguarta et al (2020), Deshmukh et al (2020.…”

Section: Cough Detectionmentioning

confidence: 98%

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

Hariri

Narin

2021

Soft Comput

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The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In this way, patients will be isolated from other people and the spread of the virus can be prevented. For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading. Since the standard testing system is time-consuming and not available for everyone, alternative early screening techniques have become an urgent need. In this study, the approaches used in the detection of COVID-19 based on deep learning (DL) algorithms, which have been popular in recent years, have been comprehensively discussed. The advantages and disadvantages of different approaches used in literature are examined in detail. We further present the databases and major future challenges of DL-based COVID-19 detection. The computed tomography of the chest and X-ray images gives a rich representation of the patient’s lung that is less time-consuming and allows an efficient viral pneumonia detection using the DL algorithms. The first step is the preprocessing of these images to remove noise. Next, deep features are extracted using multiple types of deep models (pretrained models, generative models, generic neural networks, etc.). Finally, the classification is performed using the obtained features to decide whether the patient is infected by coronavirus or it is another lung disease. In this study, we also give a brief review of the latest applications of cough analysis to early screen the COVID-19 and human mobility estimation to limit its spread.

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Section: Cough Detectionmentioning

confidence: 98%

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

Hariri

Narin

2021

Soft Comput

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“…In [23], Mohamed Bader et al proposed the significant model with the combination of Mel-Frequency Cepstral Coefficients (MFCCs) and SSP (Speech Signal Processing) to the extraction of samples from non-COVID and COVID, and it finds the person correlation from their relationship coefficients. These findings indicate high similarity between various breathing respiratory sounds and COVID cough sounds in MFCCs, although MFCC speech is more robust between non-COVID-19 samples and COVID-19 samples.…”

Section: Literature Review and Background Workmentioning

confidence: 99%

A literature review on COVID-19 disease diagnosis from respiratory sound data

Kumar¹,

Pja

2021

AIMS Bioengineering

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The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day; in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.

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“…In [45] , the authors have used the MFCC features of cough, breathing, and voice sounds to discriminate the COVID-19 patients from the non-COVID-19 patients. The authors concluded that the MFCCs of cough and breathing sounds for the COVID-19 patients and non-COVID-19 patients are similar.…”

Section: Introductionmentioning

confidence: 99%

A study of using cough sounds and deep neural networks for the early detection of Covid-19

Islam¹,

Abdel-Raheem²,

Tarique

2022

Biomedical Engineering Advances

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The current clinical diagnosis of COVID-19 requires person-to-person contact, needs variable time to produce results, and is expensive. It is even inaccessible to the general population in some developing countries due to insufficient healthcare facilities. Hence, a low-cost, quick, and easily accessible solution for COVID-19 diagnosis is vital. This paper presents a study that involves developing an algorithm for automated and noninvasive diagnosis of COVID-19 using cough sound samples and a deep neural network. The cough sounds provide essential information about the behavior of glottis under different respiratory pathological conditions. Hence, the characteristics of cough sounds can identify respiratory diseases like COVID-19. The proposed algorithm consists of three main steps (a) extraction of acoustic features from the cough sound samples, (b) formation of a feature vector, and (c) classification of the cough sound samples using a deep neural network. The output from the proposed system provides a COVID-19 likelihood diagnosis. In this work, we consider three acoustic feature vectors, namely (a) time-domain, (b) frequency-domain, and (c) mixed-domain (i.e., a combination of features in both time-domain and frequency-domain). The performance of the proposed algorithm is evaluated using cough sound samples collected from healthy and COVID-19 patients. The results show that the proposed algorithm automatically detects COVID-19 cough sound samples with an overall accuracy of 89.2%, 97.5%, and 93.8% using time-domain, frequency-domain, and mixed-domain feature vectors, respectively. The proposed algorithm, coupled with its high accuracy, demonstrates that it can be used for quick identification or early screening of COVID-19. We also compare our results with that of some state-of-the-art works.

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

Cited by 43 publications

References 10 publications

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

A literature review on COVID-19 disease diagnosis from respiratory sound data

A study of using cough sounds and deep neural networks for the early detection of Covid-19

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