# Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021

arXiv: https://arxiv.org/abs/2206.05462 · HTML: https://mlsanity.com/paper/2206.05462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 11 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics](https://mlsanity.com/paper/2103.09148/) (2021)
- [Interpretable Representation Learning for Speech and Audio Signals Based on Relevance Weighting](https://mlsanity.com/paper/2011.02136/) (2020)
- [A Multi-Head Relevance Weighting Framework For Learning Raw Waveform Audio Representations](https://mlsanity.com/paper/2107.14793/) (2021)

## Abstract (arXiv)

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. The proposed system is based on the combination of 4 different approaches, each focusing more on one aspect of the problem, and reaches the blind test AUCs of 86.41, 77.60, and 84.55, in the breathing, cough, and speech tracks, respectively, and the AUC of 85.37 in the fusion of these three tracks.