# Automated COVID-19 diagnosis
This repo contains the code created by Elien Martens during IAESTE internship summer 2021, Technical University of Kosice (Slovakia). 

## Data

The [Interspeech Computational Paralinguistics ChallengE (ComParE) 2021](vhttp://www.compare.openaudio.eu/now/) proposes two challenges related to COVID-19 detection based on audio samples. Such samples represent speech and cough audio from both healthy and infected speakers. The COVID-19 Speech Sub-Challenge (CSS) offers 3.24 hours of audio recordings containing speech samples, while the COVID19 Cough Sub-Challenge (CCS) provides 1.63 hours of cough samples.
The COVID-19 datasets can be obtained through The University of Cambridge. 

## How it works
-   clone repository
-   add cough or speech dataset (dist folder) (see [Data](##data) section), so that the structure is the following:
```
CovidSpeechChallenge
  |-- dist/
  	|-- lab/
	|-- wav/
  |-- features/
  |-- results/
  |-- src/
  |-- vggish/
  |-- run_experiments.sh
```
- run ```run_experiments.sh``` to do the feature extraction, SVM training and prediction for each features combination

## About the code
-	The ```features/``` folder will contain the extracted features after running the feature extraction codes. These are not included in this repository to limit the storage.
-	The ```results/``` contains a subfolder for each experiment with the final predictions for devel and test subset (saved as csv file) + plot of the normalized confusion matrix saved as jpg image (only for predictions test subset).
This folder of the repository contains the results for the speech audio data.
-	The ```src/``` folder contains the python files for all feature extraction and SVMs
-	The ```vggish/``` folder contains the code necessary for VGGish and is downloaded from [https://modelzoo.co/model/audioset](https://modelzoo.co/model/audioset).
-	Finally ```run_experiments.sh``` is a bash script that can be modified to only run parts of the experiments.