Date of this Version



Audio analytics; COPD; COVID-19; cough; microphone-sensing.


Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches - unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment.


This is the published version of the Vhaduri, S. V. Dibbo and Y. Kim, "Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings," in IEEE Open Journal of Engineering in Medicine and Biology, vol. 4, pp. 55-66, 2023, doi: 10.1109/OJEMB.2023.3271457.