# COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers

arXiv: https://arxiv.org/abs/2207.09529 · HTML: https://mlsanity.com/paper/2207.09529/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 84 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Focal Self-attention for Local-Global Interactions in Vision Transformers](https://mlsanity.com/paper/2107.00641/) (2021)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets](https://mlsanity.com/paper/2004.05758/) (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)
- [COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings](https://mlsanity.com/paper/2012.01926/) (2020)
- [COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images](https://mlsanity.com/paper/2006.01409/) (2020)
- [Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds](https://mlsanity.com/paper/2009.08790/) (2020)
- [Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification](https://mlsanity.com/paper/2009.07652/) (2020)
- [TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging](https://mlsanity.com/paper/2111.02163/) (2021)
- [COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19](https://mlsanity.com/paper/2005.01577/) (2020)
- [Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks](https://mlsanity.com/paper/1907.05708/) (2019)
- [Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT](https://mlsanity.com/paper/2005.03264/) (2020)
- [Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing](https://mlsanity.com/paper/2010.02417/) (2020)
- [Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks](https://mlsanity.com/paper/2012.14553/) (2020)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases](https://mlsanity.com/paper/2002.03894/) (2020)
- [Can Machine Learning Be Used to Recognize and Diagnose Coughs?](https://mlsanity.com/paper/2004.01495/) (2020)
- [Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough](https://mlsanity.com/paper/2107.10716/) (2021)
- [Interpreting glottal flow dynamics for detecting COVID-19 from voice](https://mlsanity.com/paper/2010.16318/) (2020)

## Built on by (most cited first, 4 of 4)

- [The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence](https://mlsanity.com/paper/2302.00225/) (2023)
- [An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough Audio: A Case Study for COVID-19](https://mlsanity.com/paper/2401.02996/) (2024)
- [On-Site Precise Screening of SARS-CoV-2 Systems Using a Channel-Wise Attention-Based PLS-1D-CNN Model with Limited Infrared Signatures](https://mlsanity.com/paper/2410.20132/) (2024)
- [Fine-tuning Pre-trained Audio Models for COVID-19 Detection: A Technical Report](https://mlsanity.com/paper/2511.14939/) (2025)

## Abstract (arXiv)

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its utility is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of-the-art conventional and deep-learning baselines. Demonstrations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 83% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.