Development of automatic cough monitoring, measurement and service system

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During this year we built a high performance cough detection and monitoring system and successfully evaluated it using the data collected at FMU. There are 11 patients audio recordings in the dataset with highly irregular cough events time distribution. Out approach is to segment the input data into 10sec long segments and process each segment separately. Out model is based on fine tuned large audio model called HuBERT which can identify cough frames with high accuracy. In addition, we trained a special network which estimates the probability of each identified cough frame being the first, second, etc, in the couch event. This way, we can distinguish separate cough events even when they come right one after another within a long sequence of cough frames.