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

Mahanta, Saranga Kingkor; Kaushik, Darsh; Jain, Shubham; Truong, Hoang Van; Guha, Koushik

doi:10.48550/arxiv.2110.06123

Cited by 1 publication

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“…For an example, a machine learning-based framework proposed in [6] utilized handcrafted features and Support Vector Machine (SVM) model, achieved the AUC score of 85.02 on the First DiCOVA dataset [2]. Further exploration on this dataset, a deep learning framework proposed in [7], which used the ConvNet model incorporated with Data Augmentation, achieved the best AUC score of 87.07 and presented the top-1 position in the First DiCOVA Challenge. Focusing on feature extraction, Madhu et al [8] combined the Mel-frequency cepstral coefficients (MFCC) with the delta features (i.e., the delta features are extracted from a complicated framework using Long Short-Term Memory (LSTM), Gabor filter bank, and the Teager energy operator (TEO) in the order).…”

Section: Introductionmentioning

confidence: 99%

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

Truong¹,

Pham²

2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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This paper presents a deep learning framework for detecting COVID-19 positive subjects from their cough sounds. In particular, the proposed approach comprises two main steps. In the first step, we generate a feature representing the cough sound by combining an embedding extracted from a pre-trained model and handcrafted features extracted from draw audio recording, 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 in the second step. Our experiments on the Track-2 dataset of the Second 2021 DiCOVA Challenge achieved the second top ranking with an AUC score of 81.21 and the top F1 score of 53.21 on a Blind Test set, improving the challenge baseline by 8.43% and 23.4% respectively and showing deployability, robustness and competitiveness with the state-of-the-art systems.

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