  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.
