mlsanityData to 7 Oct 2026

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

arXiv 2206.05462 ↗First posted 11 Jun 2022eess.AS cs.LG cs.SDcited by 0

Not on the map. Why?

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.

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

Builds on

7 of 9 references.

References · 7

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  2. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  3. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 606
  4. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
  5. 2021DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acousticscited by 21
  6. 2020Interpretable Representation Learning for Speech and Audio Signals Based on Relevance Weightingcited by 5
  7. 2021A Multi-Head Relevance Weighting Framework For Learning Raw Waveform Audio Representationscited by 1

Built on by

Not cited yet.