# Cough Detection Using Selected Informative Features from Audio Signals

arXiv: https://arxiv.org/abs/2108.03538 · HTML: https://mlsanity.com/paper/2108.03538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC](https://mlsanity.com/paper/2010.08770/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)

## Abstract (arXiv)

Cough is a common symptom of respiratory and lung diseases. Cough detection is important to prevent, assess and control epidemic, such as COVID-19. This paper proposes a model to detect cough events from cough audio signals. The models are trained by the dataset combined ESC-50 dataset with self-recorded cough recordings. The test dataset contains inpatient cough recordings collected from inpatients of the respiratory disease department in Ruijin Hospital. We totally build 15 cough detection models based on different feature numbers selected by Random Frog, Uninformative Variable Elimination (UVE), and Variable influence on projection (VIP) algorithms respectively. The optimal model is based on 20 features selected from Mel Frequency Cepstral Coefficients (MFCC) features by UVE algorithm and classified with Support Vector Machine (SVM) linear two-class classifier. The best cough detection model realizes the accuracy, recall, precision and F1-score with 94.9%, 97.1%, 93.1% and 0.95 respectively. Its excellent performance with fewer dimensionality of the feature vector shows the potential of being applied to mobile devices, such as smartphones, thus making cough detection remote and non-contact.