A Scalable and Domain Adaptive Respiratory Symptoms Detection Framework using Earables-Reference-Cited by-同舟云学术

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A Scalable and Domain Adaptive Respiratory Symptoms Detection Framework using Earables

Published:2021-12-15 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Big Data (Big Data)
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Author:

Nguyen Nhan,Chakma Avijoy,Roy Nirmalya

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9671263/9671273/09671796.pdf?arnumber=9671796

Reference19 articles.

1. eSense Veers

2. Deep visual domain adaptation: A survey

3. A Systematic Study of Unsupervised Domain Adaptation for Robust Human-Activity Recognition

4. Unsupervised domain adaptation by backpropagation;ganin;Proceedings of The 32nd International Conference on Machine Learning,2015

5. librosa: Audio and Music Signal Analysis in Python

Cited by 3 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. The EarSAVAS Dataset;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-05-13

2. EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing;2024 IEEE Coupling of Sensing & Computing in AIoT Systems (CSCAIoT);2024-05-13

3. Sensing with Earables;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2022-09-06

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