Abstract
Coughing is one of the most frequent presenting symptoms of many diseases affecting the airways and the lungs of both humans and animals. In piggeries, the continuous on-line monitoring of cough sound can be used to build an intelligent alarm system for the early detection of diseases. In a first study, with experiments under laboratory conditions, algorithms have been developed to detect cough sounds and to classify the animals whether they were ill or not. In this study, the algorithm was tested in field conditions. Pig cough sounds were registered on 44, 150 days old, 60 kg heavy Landrace x Large White x Duroc crosses, by an operator holding a microphone at about 20 to 50 cm from the pigs head. From these sound files, feature vectors were extracted, containing information on the sound energy, spectral properties and time derivates. These feature vectors were compared to a reference set by means of a dynamic time warping algorithm. This leads to a two class classification: ill, no ill. The classification was checked by a veterinarian and found to be correct in 86 % of the cases.
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