  Cough sounds act as an important indicator of an individual's physical
health, often used by medical professionals in diagnosing a patient's ailments.
In recent years progress has been made in the area of automatically detecting
cough events and, in certain cases, automatically identifying the ailment
associated with a particular cough sound. Ethical and sensitivity issues
associated with audio recordings of coughs makes it more difficult for this
data to be made publicly available. However, without the public availability of
a reliable database of cough sounds, developments in the area of audio event
detection are likely to be hampered. The purpose of this paper is to spread
awareness of a database containing a large amount of naturally occurring cough
sounds that can be used for the implementation, evaluation, and comparison of
new machine learning algorithms that allow for audio event detection associated
with cough sounds. Using a purpose built GUI designed in MATLAB, the
re-annotation procedure followed a reusable methodology that allowed for quick
and efficient importing and marking of audio signals, resulting in a
re-annotated version of the Augmented Multi-party Interaction (AMI) corpus'
cough location annotations, with 1369 individual cough events. All cough
annotations and the re-annotation tool are made available for download and
public use.
