An Overview on Audio, Signal, Speech, &amp; Language Processing for COVID-19

Deshpande, Gauri; Schuller, Björn

doi:10.48550/arxiv.2005.08579

Cited by 19 publications

(19 citation statements)

References 20 publications

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“…Researchers and scientists have long recognized the utility of sound as a potential predictor of actions and health. For example, independent audio recorders [22] were used for the reason in digital stethoscopes to identify sounds from the human respiratory. Highly trained clinicians felt that compared to other methodologies such as MRI (Magnetic Resonance Imaging), and sonography, it was effortless to listen to sounds and then interpret them.…”

Section: Background Workmentioning

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

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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%

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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“…With the advent of COVID-19, researchers have begun to explore whether cough sounds could be diagnostic (15). A COVID-19-related cough detection study is presented in (15) using a cohort of 48 patients tested with COVID-19 versus other pathological coughs, in which a set of models is trained.…”

Section: Iatos: Proposal and Backgroundmentioning

confidence: 99%

IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples

Pizzo¹,

Esteban²

2021

Preprint

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OBJECTIVE Our objective is to evaluate the possibility of using cough audio recordings (spontaneous or simulated) to detect sound patterns in people who are diagnosed with COVID-19. The research question that led our work was: what is the sensitivity and specificity of a machine learning based COVID-19 cough classifier, using RT-PCR tests as gold standard?DESIGN Diagnostic test validation study. SETTING The audio samples that were collected for this study belong to individuals who were swabbed in the City of Buenos Aires in 20 public and 1 private facilities where RT-PCR studies were carried out on patients suspected of COVID, and 14 out-of-hospital isolation units for patients with confirmed COVID mild cases. The audios were collected through the Buenos Aires city government WhatsApp chatbot that was specifically designed to address citizen inquiries related to the coronavirus pandemic .PARTICIPANTS The data collected corresponds to 2821 individuals who were swabbed in the City of Buenos Aires, between August 11 and December 2, 2020. Individuals were divided into 1409 that tested positive for COVID-19 and 1412 that tested negative. From this sample group, 52.6% of the individuals were female and 47.4% were male. 2.5% were between the age of 0 and 20 , 61.1% between the age of 21 and 40 , 30.3% between the age of 41 and 60 and 6.1% were over 61 years of age.MAIN OUTCOME MEASURES The audio files corresponding to each individual were categorized as "positive" and "negative" based on their PCR swab test results.RESULTS Using the dataset of 2821 individuals our results showed that the neural network classifier was able to discriminate between the COVID-19 positive and the healthy coughs with an accuracy of 88%. This accuracy obtained during the training process was later tested and confirmed with a second dataset corresponding to 492 individuals.CONCLUSION Based on the reported results, we consider that the cough audio analysis could be used as a COVID-19 digital pre-entry screening tool to be used prior to conventional testing tools such as RT-PCR tests. This could help reduce wait times, ease stress, and lower exposure risk of healthcare personnel and other citizens.

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“…Recently, with the advent of COVID-19, researchers have started to explore if respiratory sounds could be diagnostic [10]. In [16] digital stethoscope data from lung auscultation is used as a diagnostic signal for COVID-19.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Brown,

Chauhan,

Grammenos

et al. 2020

Preprint

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Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as diagnostic or progression indicators for diseases and disease onset. However, until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC above 70% across all tasks. Clearly these results are preliminary and only scratch the surface of the possibilities of the exploitation of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.

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An Overview on Audio, Signal, Speech, & Language Processing for COVID-19

Cited by 19 publications

References 20 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

IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

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