Effect of Noise on Generic Cough Models
Sayanton V. Dibbo, Yugyeong Kim, Sudip Vhaduri
Abstract
Respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and asthma, are two major reasons for people's death across the globe. In addition to these common inflammatory respiratory diseases, some human transmissible respiratory diseases, such as coronaviruses, cause a global pandemic. One major symptom of these inflammatory respiratory diseases is coughing. Identifying coughing using smartphone-microphone recordings is easily doable from a remote setup and can help physicians and researchers early guess a situation for an individual and a community. However, smartphone-microphone recordings can be affected by environmental noises and that can impact the performance of models that are developed to detect coughing from microphone recording. Thereby, in this work, we present a detailed analysis of noise impacts on cough detection models. We develop models using voluntary coughs and other background sounds obtained from three public datasets and test the performance of those models while detecting various types of coughs, including COPD and COVID-19, obtain from three separate datasets in the presence of background noises.