Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview

December 08, 2020 ยท The Cartographer ยท ๐Ÿ› Global Conference on Life Sciences and Technologies

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

"No code URL or promise found in abstract"
"Title-pattern auto-detect: Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview"

Evidence collected by the PWNC Scanner

Authors Kun Qian, Bjorn W. Schuller, Yoshiharu Yamamoto arXiv ID 2012.04650 Category cs.SD: Sound Cross-listed cs.LG, eess.AS Citations 16 Venue Global Conference on Life Sciences and Technologies Last Checked 2 days ago
Abstract
Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e.g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds). Nevertheless, CA has been underestimated in the considered data-driven technologies for fighting the COVID-19 pandemic caused by the SARS-CoV-2 coronavirus. In this light, summarise the most recent advances in CA for COVID-19 speech and/or sound analysis. While the milestones achieved are encouraging, there are yet not any solid conclusions that can be made. This comes mostly, as data is still sparse, often not sufficiently validated and lacking in systematic comparison with related diseases that affect the respiratory system. In particular, CA-based methods cannot be a standalone screening tool for SARS-CoV-2. We hope this brief overview can provide a good guidance and attract more attention from a broader artificial intelligence community.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Sound