Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition

Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition

Coughs sounds have shown promising as a potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations from cough samples. Unlike most traditional schemes such as mere maxing or...

Full description

Bibliographic Details
Main Authors: Ren Zhao, Chang Yi, Nejdl Wolfgang, Schuller Björn W.
Format: Article
Language:English
Published: EDP Sciences 2022-01-01
Series:Acta Acustica
Subjects:
covid-19
cough sound
ensemble learning
attention mechanism
complementary representation
Online Access:https://acta-acustica.edpsciences.org/articles/aacus/full_html/2022/01/aacus210070/aacus210070.html

Internet

https://acta-acustica.edpsciences.org/articles/aacus/full_html/2022/01/aacus210070/aacus210070.html

Similar Items