  This paper addresses the issue of cough detection using only audio
recordings, with the ultimate goal of quantifying and qualifying the degree of
pathology for patients suffering from respiratory diseases, notably
mucoviscidosis. A large set of audio features describing various aspects of the
audio signal is proposed. These features are assessed in two steps. First,
their intrisic potential and redundancy are evaluated using mutual
information-based measures. Secondly, their efficiency is confirmed relying on
three classifiers: Artificial Neural Network, Gaussian Mixture Model and
Support Vector Machine. The influence of both the feature dimension and the
classifier complexity are also investigated.
