The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

Orlandic, Lara; Teijeiro, Tomas; Atienza, David

doi:10.48550/arxiv.2009.11644

Cited by 14 publications

(23 citation statements)

References 14 publications

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“…In [25], Orlandic L et al implemented the "COUGHVID" crowdsourced dataset for cough analysis with COVID-19 symptom; More than twenty thousand crowdsourced cough recordings reflected a broad range of topic gender, age, geographic locations, and COVID-19 status was given in the COUGHVID dataset. They have collected a series of 121 cough sounds and 94 no-cough sounds first-hand to train the classifier includes voice, laughter, silence, and various background noises [26].…”

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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“…This ensures higher quality ground truth labels, avoids potential target leakage into the self-reported data and cough samples due to subliminal effects of an aforeknown diagnosis [30], and eliminates issues related to spectral characteristics of the audio recordings made on different hardware with different software filtering and compression. Other studies crowdsource the data through web or mobile apps, which is a less expensive and time-consuming option that yields much larger datasets, albeit of lesser quality both in the ground truth infection status labels and the audio recordings themselves, [33,34].…”

Section: A Related Workmentioning

confidence: 99%

“…As of the time of writing, only three large cough datasets featuring COVID-19 positive samples were publicly available -the EPFL COUGHVID dataset [34], Coswara [39], and Covid19-Cough [44]. The EPFL dataset comprises of approximately 20000 records.…”

Section: A Open Datasetsmentioning

confidence: 99%

Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough

Ponomarchuk,

Burenko,

Malkin

et al. 2021

Preprint

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The COVID-19 pandemic created a significant interest and demand for infection detection and monitoring solutions.In this paper we propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach combines signal processing methods with finetuned deep learning networks and provides methods for signal denoising, cough detection and classification. We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection. The application showed robust performance on both open sourced datasets and on the noisy data collected during beta testing by the end users.

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“…During the pandemic, many crowdsourcing platforms (such as COUGHVID 2 [24], COVID Voice Detector 3 , and COVID-19 Sounds App 4 ) have been designed to gather respiratory sound audios from both healthy and COVID-19 positive groups for the research purpose. With these collected datasets, researchers in the artificial intelligence community have started to develop machine learning and deep learning based methods (e.g., [5,12,17,25,27]) for cough classification to detect COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification

Xue,

Salim

2021

Preprint

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The usage of smartphone-collected respiratory sound, trained with deep learning models, for detecting and classifying COVID-19 becomes popular recently. It removes the need for in-person testing procedures especially for rural regions where related medical supplies, experienced workers, and equipment are limited. However, existing sound-based diagnostic approaches are trained in a fullysupervised manner, which requires large scale well-labelled data. It is critical to discover new methods to leverage unlabelled respiratory data, which can be obtained more easily. In this paper, we propose a novel self-supervised learning enabled framework for COVID-19 cough classification. A contrastive pre-training phase is introduced to train a Transformer-based feature encoder with unlabelled data. Specifically, we design a random masking mechanism to learn robust representations of respiratory sounds. The pre-trained feature encoder is then fine-tuned in the downstream phase to perform cough classification. In addition, different ensembles with varied random masking rates are also explored in the downstream phase. Through extensive evaluations, we demonstrate that the proposed contrastive pre-training, the random masking mechanism, and the ensemble architecture contribute to improving cough classification performance. CCS CONCEPTS• Applied computing → Sound and music computing; Health informatics; • Information systems → Data mining.

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The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

Cited by 14 publications

References 14 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

Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough

Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification

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