Detecting COVID-19 from Audio Recording of Coughs Using Random Forests and Support Vector Machines

Abstract

The detection of COVID-19 is and will remain in the foreseeable future a crucial challenge, making the development of tools for the task important. One possible approach, on the confines of speech and audio processing, is detecting potential COVID-19 cases based on cough sounds. We propose a simple, yet robust method based on the well-known ComParE 2016 feature set, and two classical machine learning models, namely Random Forests, and Support Vector Machines (SVMs). Furthermore, we combine the two methods, by calculating the weighted average of their predictions. Our results in the DiCOVA challenge show that this simple approach leads to a robust solution while producing competitive results. Based on the Area Under the Receiver Operating Characteristic Curve (AUC ROC) score, both classical machine learning methods we applied markedly outperform the baseline provided by the challenge organisers. Moreover, their combination attains an AUC ROC score of 85.21, positioning us at fourth place on the leaderboard (where the second team attained a similar, 85.43 score). Here, we would describe this system in more detail, and analyse the resulting models, drawing conclusions, and determining future work directions.

CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds

“…The main purpose of this study is to introduce and prove the usefulness of the transfer learning framework CovNet, instead of competing with the state-of-the-art performance on the DiCOVA Track-1 dataset ( 54 – 56 ) and ComParE CCS dataset ( 19 ). The constructed four CNN models are so simple that each of them only contains three convolutional layers/blocks; we do not apply any data augmentation techniques and the only input to the networks are the original log Mel spectrograms.…”

Towards sound based testing of COVID-19—Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge

“…The challenge turnaround time was days, and the progress made by different teams in this short time span highlighted their efforts. Eleven studies pursued in this challenge ( Muguli et al, 2021 , Das et al, 2021 , Mallol-Ragolta et al, 2021 , Ritwik et al, 2021 , Deshpande and Schuller, 2021 , Karas and Schuller, 2021 , Bhosale et al, 2021 , Södergren et al, 2021 , Harvill et al, 2021 , Kamble et al, 2021 , Avila et al, 2021 ), after going through the peer review process, were presented at the DiCOVA Special Session, Interspeech 2021 Conference (on Aug 2021).…”

“…The team ( Södergren et al, 2021 ) explored classical machine learning models like random forests (RF), support vector machines (SVM), and multi-layer perceptron (MLP) rather than deep learning models. The features used were the dimensional openSMILE functional features ( Eyben et al, 2010 ).…”

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

“…Regarding handcrafted features, 64 Mel-frequency cepstral coefficients (MFCCs), 12 Chromatic (Chroma), 128 Mel Spectrogram (Mel), 1 Zero-Crossing rate, 1 Gender, and 1 Duration are utilized in this paper. These handcrafted features are used as they are popularly adopted in speech processing and show robustness in the First 2021 DiCOVA Challenge [8], [9], [6]. To extract these handcrafted features, python-based Librosa Toolkits [16], a powerful library of audio signal processing, is used in this paper with the window size, FFT number, hop size set to 2048, 2048, 512.…”

“…Focusing on the cough sound, recent researchers show that it is potential to detect COVID-19 through evaluating coughing. 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.…”