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A Universal System for Cough Detection in Domestic Acoustic Environments

Automated cough detection may provide valuable clinical information for monitoring a patient’s health condition. In this paper, we present a cough detection system that utilises an acoustic onset detector as a pre-processing step, aiming to detect impulsive patterns in the audio stream. In a subsequent step, discrimination of coughing events from other impulsive sounds is handled as a binary classification task. In contrast to existing works, the proposed cough discrimination models are trained and tested with heterogeneous data uploaded by different users to online audio repositories. In that way, our system achieves robust performance to a wide range of audio recording devices and to varying noise and/or reverberation conditions. Our evaluation results showed that a sensitivity in the order of 90% and a specificity in the order of 99% can be achieved in a domestic environment with the utilization of Long-Short-Term-Memory deep neural network architecture.