cam-mobsys/covid19-sounds-neurips

COVID19 Sounds Dataset Supplementary Material

COVID-19 Sounds Dataset Supplementary Material

This repository contains the supplementary material accompanying the paper named: COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening.

Introduction

Through crowdsourcing and the help of thousands of volunteers we started the COVID-19 Sounds project. The aim of this research is to collect data to inform the diagnosis of COVID-19 by developing machine learning algorithms, based primarily on sounds of their voice, their breathing and coughing. For more information you can check out the project website here. If you wish to contact the team, you can do so at: [email protected]

Dataset

Naturally, data of such origin are sensitive in nature. Thus, to gain access to the full dataset, a Data Transfer Agreement (DTA) needs to be signed. However, in order to enable reproducibility easier we make available two small subsets used for the main tasks in the paper and are available in addition to the full dataset. Moreover, we provide the metadata and overall statistics of the dataset in this notebook. The input files for this notebook include raw location information and therefore cannot be shared for privacy reasons. Further, a notebook with more granular information regarding the samples of each platform is provided here.

Code

Our models are implemented in Python 3 (tested using 3.6 and 3.7) using Tensorflow 1.15. Please note that due to the requirement of Tensorflow 1.15 versions of python above 3.7 are not supported. Before you start, please ensure that you have requested and downloaded the task files as they are not included when cloning the repository. After receiving the files and in order to reproduce the results, you need to create an appropriate virtual environment and activate it. You can create such an environment, as is shown below:

# create a virtual environment for this project (assuming you are in covid19-sounds-neurips)
python3 -m venv ./venv
# now activate it
source ./venv/bin/activate
# and finally, install the dependencies
pip install -r ./requirements.txt

As described in the paper, two tasks are contained. We will now describe how to reproduce each of the tasks using the data provided.

Benchmarks

For both tasks, we implemented three baselines, namely:

• OpenSMILE+SVM,
• pre-trained VGGish, and a
• fine-tuned VGGish.

Transfer learning is applied to our tasks through a pre-trained CNN model named VGGish. To help the reader to better understand the architectures used, we provide a simple illustration of the models used below:

On the left of the figure we present the OpenSMILE+SVM while on the right we show the VGGish based model. We note that the fine-tuned VGGish is the same architecture but with we fix the parameters of VGGish and jointly fine-tune its backbone while also update the fully connected layers.

Task 1: Respiratory symptom prediction

This task aims at exploring the potential of various sound types in predicting respiratory abnormalities, where the symptomatic group consists of participants who reported any respiratory symptoms, including dry cough, wet cough, fever, sore throat, shortness of breath, runny nose, headache, dizziness, and chest tightness, while asymptomatic controls are those who reported no symptoms. To reproduce, please follow the steps below:

1. Navigate to the cloned repository (normally, covid19-sounds-neurips)
2. Ensure you have downloaded the task1 files from Google Drive
   • Unzip and copy/move the data_0426_en_task1.csv and 0426_EN_used_task1 in task1 files to ./Respiratory_prediction/data
3. OpenSMILE+SVM
   • Go to the path cd ./Respiratory_prediction/Opensmile
   • Extract features python 1_extract_opensmile.py Note, you can skip this, see below.
   • Perform classification python 2_classifcation.py
4. Pre-trained VGGish

   • Prepare input:

      cd ./Respiratory_prediction/data
      python pickle_data.py
      python pickle_user.py

   • Go to model's path cd ./Respiratory_prediction/model

   • Train the model sh run_train_frozen.sh

   • Test the model sh run_test_frozen.sh

5. Fine-tuned VGGish

   • Prepare input:

      cd ./Respiratory_prediction/data
      python pickle_data.py
      python pickle_user.py

     Note: If you have done already this skip in the pre-trained VGGish model, you can skip it here.

   • Go to model's path cd ./Respiratory_prediction/model

   • Train the model sh run_train.sh

   • Test the model sh run_test.sh

Running the 1_extract_opensmile.py script requires openSMILE. However, you can skip this by copying the already extracted feature csv from task1/opensmile files to this path (namely, ./Respiratory_prediction/Opensmile).

Results are summarised in Table 2:

Task 2: COVID-19 prediction

This task aims to distinguish between participants who reported a COVID-19 positive status and those who reported testing negative. Note that the positive group may show no symptoms as there are many asymptomatic COVID-19 cases, while the negative group may show typical respiratory symptoms which are not caused by an active COVID-19 infection. To reproduce, please follow the steps below:

1. Navigate to the cloned repository (normally, covid19-sounds-neurips)
2. Ensure you have downloaded the task2 files
   • Unzip and copy/move the data_0426_en_task2.csv and 0426_EN_used_task2 in task2 files to ./COVID19_prediction/data
3. OpenSMILE+SVM
   • Go to the path cd ./COVID19_prediction/Opensmile
   • Extract features python 1_extract_opensmile.py. Note: as above, you can skip this by copying the extracted feature csv from task2/opensmile files to this path.
   • Classification python 2_classifcation.py
4. Pre-trained VGGish

   • Prepare input

      cd ./COVID19_prediction/data
      python pickle_data.py

   • Go to model's path cd ./COVID19_prediction/COVID_model

   • Train the model sh run_train_frozen.sh

   • Test the model sh run_test_frozen.sh

5. Fine-tuned VGGish

   • Prepare input

      cd ./COVID19_prediction/data
      python pickle_data.py

     Note: If you have done already this skip in the pre-trained VGGish model, you can skip it here.

   • Go to model's path cd ./COVID19_prediction/COVID_model

   • Train the model sh run_train.sh

   • Test the model sh run_test.sh

Results are summarised in Table 3:

Automatic audio quality check

In order to decide if a provided audio sample is of sufficient quality to be used for inference we provide a tool that automatically detects whether a sample is indeed of high-quality. This tool employs another known network, namely Yamnet. The sample should contain either:

• breathing (will be tagged with 'b'),
• cough (will be tagged with 'c'),
• or voice (will be tagged with 'v').

Silent and noisy samples will be filtered accordingly and labelled as 'n'. This labelling will exclude such files from further experiments. We have already prepared the annotations for all samples within the provided dataset (see Voice check, Cough check, Breath check fields in results_raw_20210426_lan_yamnet_android/ios/web_noloc.csv). Nevertheless, we make the tool available in case one wishes to follow a different data selection process. Please note that this tool requires a different environment to be used, as Yamnet requires Tensorflow 2.0. For more information please see the respective README.

Issues

The code was initially created by Tong Xia ([email protected]), Dimitris Spathis ([email protected]), and Andreas Grammenos ([email protected]). For any questions, please either contact the authors directly or create an issue.

covid19-sounds-neurips's Issues

I have one question about the FRAME_LEN and HOP

Hi team,

Thanks for your excellent work and share to the community.

I have one question about the FRAME_LEN and HOP

In your code:

SR = 16000  # sample rate
SR_VGG = 16000  # VGG pretrained model sample rate
FRAME_LEN = int(SR / 10)  # 100 ms
HOP = int(FRAME_LEN / 2)  # 50%overlap, 5ms

In this case, should the FRAME_LEN and HOP be 1600 and 800 instead of 100ms and 5ms ?

I am confused about this part.

Thanks