# Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

arXiv: https://arxiv.org/abs/2112.07285 · HTML: https://mlsanity.com/paper/2112.07285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2004.04931/) (2020)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network](https://mlsanity.com/paper/1904.08990/) (2019)
- [Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough](https://mlsanity.com/paper/2011.13320/) (2020)
- [An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety](https://mlsanity.com/paper/2005.00096/) (2020)
- [Detection of COVID-19 through the analysis of vocal fold oscillations](https://mlsanity.com/paper/2010.10707/) (2020)
- [Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner](https://mlsanity.com/paper/2002.05534/) (2020)
- [Randomly weighted CNNs for (music) audio classification](https://mlsanity.com/paper/1805.00237/) (2018)
- [A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification](https://mlsanity.com/paper/1810.07088/) (2018)
- [COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network](https://mlsanity.com/paper/2009.05096/) (2020)
- [Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC](https://mlsanity.com/paper/2010.08770/) (2020)
- [An Overview on Audio, Signal, Speech, & Language Processing for COVID-19](https://mlsanity.com/paper/2005.08579/) (2020)
- [COVID-19 Patient Detection from Telephone Quality Speech Data](https://mlsanity.com/paper/2011.04299/) (2020)
- [Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device](https://mlsanity.com/paper/2004.06912/) (2020)
- [Speech-Based Parameter Estimation of an Asymmetric Vocal Fold Oscillation Model and Its Application in Discriminating Vocal Fold Pathologies](https://mlsanity.com/paper/1910.08886/) (2019)

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

- [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022)

## Abstract (arXiv)

The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models.