Name
Title | ||
|---|---|---|
DiCOVA Challenge - Dataset, Task, and Baseline System for COVID-19 Diagnosis Using Acoustics. |
Abstract | ||
|---|---|---|
The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning. This challenge is an open call for researchers to analyze a dataset of sound recordings collected from COVID-19 infected and non-COVID-19 individuals for a two-class classification. These recordings were collected via crowdsourcing from multiple countries, through a website application. The challenge features two tracks, one focuses on using cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings. In this paper, we introduce the challenge and provide a detailed description of the dataset, task, and present a baseline system for the task. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.21437/Interspeech.2021-74 | Interspeech |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ananya Muguli | 1 | 0 | 0.68 |
| Lancelot Pinto | 2 | 0 | 0.34 |
| Nirmala R. | 3 | 0 | 0.34 |
| Neeraj Sharma | 4 | 10 | 5.76 |
| Krishnan Prashant | 5 | 7 | 1.65 |
| Ghosh, Prasanta Kumar | 6 | 8 | 10.20 |
| Rohit Kumar | 7 | 2 | 3.92 |
| Shreyas Ramoji | 8 | 2 | 1.05 |
| Shrirama Bhat | 9 | 0 | 0.34 |
| Srikanth Raj Chetupalli | 10 | 0 | 1.69 |
| Sriram Ganapathy | 11 | 252 | 39.62 |
| Viral Nanda | 12 | 0 | 0.34 |