COVID-19 Detection Exploiting Self-Supervised Learning Representations of Respiratory Sounds-Reference-Cited by-同舟云学术

COVID-19 Detection Exploiting Self-Supervised Learning Representations of Respiratory Sounds

Published:2022-09-27 Issue: Volume: Page:
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Container-title:2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
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Author:

Mallol-Ragolta Adria¹,Liu Shuo¹,Schuller Bjorn¹

Affiliation:

1. University of Augsburg,EIHW - Chair of Embedded Intelligence for Health Care & Wellbeing,Germany

Funder

DFG

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9926747/9926739/09926967.pdf?arnumber=9926967

Reference14 articles.

1. Coswara — A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis

2. The Second Dicova Challenge: Dataset and Performance Analysis for Diagnosis of Covid-19 Using Acoustics

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4. Opensmile

5. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations;baevski;Proc of the 34th conf on Neural Information Processing Systems,2020

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