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Found 6 papers, 2 papers with code
Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 Detection
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection.
Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals
The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants.
Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021
This report describes the system used for detecting COVID-19 positives using three different acoustic modalities, namely speech, breathing, and cough in the second DiCOVA challenge.
Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches.
The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics
This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals.
A Multi-Head Relevance Weighting Framework For Learning Raw Waveform Audio Representations
The relevance weighted representations are fed to a neural classifier and the whole system is trained jointly for the audio classification objective.
