COVID-19 Detection From Audio Signals Using LR-MLP-RF-GMM Classifiers

Abstract

The COVID-19 pandemic bestows global challenges surpassing boundaries of country, religion race, and economy. Testing of COVID-19 patients conditions are remains a challenging task due to the lack of adequate medical supplies, well-trained personnel and conducting reverse transcription polymerase chain reaction (RT-PCR) testing is expensive, long-drown-out process violates social distancing. In this direction, we used microbiologically confirmed COVID-19 dataset based on cough recordings from Coswara dataset. The Coswara dataset is also one of the open challenge dataset for researchers to investigate sound recordings of the Coswara dataset, collected from COVID-19 infected and non-COVID-19 individuals, for classification between Positive and Negative detection. These COVID-19 recordings were collected from multiple countries, through the provided crowd-sourcing website. Here, our work mainly focuses on cough sound based recordings. The dataset is released open access. We developed an acoustic biosignature feature extractors to screen for potential problems from cough recordings, and provide personalized advice to a particular patient's state to monitor his suitable condition in real-time. In our work, cough sound recordings are converted into Mel Frequency Cepstral Coefficients (MFCCs) and passed through a Gaussian Mixture Model (GMM) based pattern recognition, decision making based on a binary pre-screening diagnostic. When validated with infected and non-infected patients, for a two-class classification, using a Coswara dataset. The GMM is applied for developing a model for detection of biomarker based detection and achieves COVID-19 and non-COVID-19 patients accuracy of 73.22% based on the Coswara dataset and also compared with existing classifiers.