Sound Dr -- A database of Respiratory Sound and Baseline System for COVID-19 Detection

As the COVID-19 pandemic significantly affect every aspects of human life, it is urgent to provide a data for further researches. We, therefore, introduce a dataset named Sound Dr Database which provides not only quality sounds of coughing and breathing but also metadata for relevant-respiratory illness or diseases. Creating proof-of-concept systems is effective for the detection of abnormalities in the respiratory sounds of patients. These solutions will serve as effective tools to assist physicians in diagnosing respiratory disorders.

A Cough-based deep learning framework for detecting COVID-19

In this paper, we propose a deep learning-based framework for detecting COVID-19 positive subjects from their cough sounds. In particular, the proposed framework comprises two main steps. In the first step, we generate a feature representing the cough sound by combining embedding features extracted from a pre-trained model and handcrafted features, referred to as the front-end feature extraction. Then, the combined features are fed into different back-end classification models for detecting COVID-19 positive subjects. The experimental results on the Second 2021 DiCOVA Challenge - Track 2 dataset achieve the top-2 ranking with an AUC score of 81.21 on the blind Test set, improving the challenge baseline by 6.32 and showing competitive with the state-of-the-art systems.

COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation

With the periodic rise and fall of COVID-19 and countries being inflicted by its waves, an efficient, economic, and effortless diagnosis procedure for the virus has been the utmost need of the hour. COVID-19 positive individuals may even be asymptomatic making the diagnosis difficult, but amongst the infected subjects, the asymptomatic ones need not be entirely free of symptoms caused by the virus. They might not show any observable symptoms like the symptomatic subjects, but they may differ from uninfected ones in the way they cough. These differences in the coughing sounds are minute and indiscernible to the human ear, however, these can be captured using machine learning-based statistical models. In this paper, we present a deep learning approach to analyze the acoustic dataset provided in Track 1 of the DiCOVA 2021 Challenge containing cough sound recordings belonging to both COVID-19 positive and negative examples. To perform the classification on the sound recordings as belonging to a COVID-19 positive or negative examples, we propose a ConvNet model. Our model achieved an AUC score percentage of 72.23 on the blind test set provided by the same for an unbiased evaluation of the models. The ConvNet model incorporated with Data Augmentation further increased the AUC-ROC percentage from 72.23 to 87.07. It also outperformed the DiCOVA 2021 Challenge's baseline model by 23% thus, claiming the top position on the DiCOVA 2021 Challenge leaderboard. This paper proposes the use of Mel frequency cepstral coefficients as the feature input for the proposed model.