Comparison of Silence Removal Methods for the Identification of Audio Cough Events

Sensing technologies are embedded in our everyday lives. Smart homes typically use an Audio Virtual Assistant (AVA) (e.g. Alexa, Siri, and Google Home) interface that collects sensor information, which can provide security, assist in everyday activities and monitor health related information. One such measure is cough, changes of which can be a marker of worsening conditions for many respiratory diseases. Creating a reliable monitoring system utilizing technology that may already be present in the home (i.e. AVA) may provide an opportunity for early intervention and reductions in the number of long-term hospitalizations. This paper focuses on the optimization of the silence removal and segmentation step in an at home setting with low to moderate background noise to identify cough events. Three commonly used methods (Standard deviation (SD), Short-term Energy (SE), Zero-crossing rate (ZCR)) were compared to manual segmentations. Each method was applied to 209 audio files that were manually verified to contain at least one cough event and the average segmentation accuracy, over segmentation and under segmentation results were compared. The ZCR method had the highest accuracy (89%); however, it completely failed under moderate noise conditions. The SD method had the best combination of accuracy (86%), ability to perform under noisy conditions and low prevalence of over and under segmentation (22% and 15% respectively). Therefore, we recommend using an adaptive approach to silence removal among cough events based on the level of background noise (i.e use the ZCR method when the background noise is low and the SD method when it is higher) prior to implementation of a cough classification system.