Audio-based cough counting using independent subspace analysis
This addresses the need for efficient cough monitoring in ambulatory settings, though it is incremental as it builds on existing independent subspace analysis methods.
The paper tackles the problem of automatically detecting cough events in audio recordings to reduce manual counting time, achieving a true positive rate of 76% with an average of 2.85 false positives per minute.
In this paper, an algorithm designed to detect characteristic cough events in audio recordings is presented, significantly reducing the time required for manual counting. Using time-frequency representations and independent subspace analysis (ISA), sound events that exhibit characteristics of coughs are automatically detected, producing a summary of the events detected. Using a dataset created from publicly available audio recordings, this algorithm has been tested on a variety of synthesized audio scenarios representative of those likely to be encountered by subjects undergoing an ambulatory cough recording, achieving a true positive rate of 76% with an average of 2.85 false positives per minute.