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

Bader, Mohamed; Shahin, Ismail; Hassan, Abdelfatah

doi:10.48550/arxiv.2010.08770

Cited by 2 publications

(3 citation statements)

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“…In [17], Bader M et al proposed a significant model with the combination of Mel-Frequency Cepstral Coefficients (MFCCs) and SSP (Speech Signal Processing) to extract samples from non-COVID and COVID and find 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: Background Workmentioning

confidence: 99%

“…Finally, using a 'Softmax' classifier, the processed signals are categorized. The DDAE is derived in-depth feature respiratory sound signals in contrast with the standard MFCC feature extraction method [17][18][19]; the usefulness of the fundamental function of this analysis throughout the classification of respiratory sounds is thus demonstrated. The accuracy of classification with 1D CNN has a high '𝐹 1 ' Score which for the diagnosis of COVID-19 is slightly increased.…”

Section: Introductionmentioning

confidence: 99%

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Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kumar¹,

Pja

2021

AIMS Public Health

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The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models. Results As a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds. Conclusion A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.

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Section: Background Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kumar¹,

Pja

2021

AIMS Public Health

View full text Add to dashboard Cite

show abstract

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

Cited by 2 publications

References 0 publications

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

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

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