The DiCOVA 2021 Challenge — An Encoder-Decoder Approach for COVID-19 Recognition from Coughing Audio-Reference-Cited by-同舟云学术

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The DiCOVA 2021 Challenge — An Encoder-Decoder Approach for COVID-19 Recognition from Coughing Audio

Published:2021-08-30 Issue: Volume: Page:
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Container-title:Interspeech 2021
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Author:

Deshpande Gauri,Schuller Björn W.

Publisher

ISCA

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Neural-symbolic temporal decision trees for multivariate time series classification;Information and Computation;2024-12

2. COVID-19 Detection Systems Based on Speech and Image Data Using Deep Learning Algorithms;International Journal of Computational Intelligence Systems;2024-09-10

3. Automatic Breathing Pattern Analysis from Reading-Speech Signals;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24

4. The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests;Artificial Intelligence in Medicine;2023-03

5. Analysing Breathing Patterns in Reading and Spontaneous Speech;Speech and Computer;2023

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