Automatic detection of continuous pig cough in a complex piggery environment

Abstract

Pig cough sound monitoring is an effective means of early warning for respiratory diseases. Until present, most studies focused on the investigation of high precision pig cough recognition algorithms based on manually segmented individual sound datasets. However, the recognition of continuous sound was mostly ignored, which cannot apply in practical engineering. Meanwhile, less consideration has been given to complex scenarios, such as the overlap of multiple sounds, which occur frequently in large-scale piggeries. To this end, we explored an automatic detection of continuous sound algorithm and proposed a continuous pig cough recognition method which has a significant role in the diagnosis of diseases. Initially, we proposed a voice activity detection (VAD) method to automatically segment continuous sound. Subsequently, we investigated a multi-classifier fusion strategy to promote recognition accuracy. Finally, we proposed a low-complexity continuous pig cough recognition method. The experimental results show that the recall and precision of pig cough in continuous sound is 93.1% and 91.6% respectively, which is much higher than the 67.3% and 90.6% of the baseline detection method. The recognition accuracy of continuous pig cough reached 91.4%. From the perspective of practical application, our algorithm development considered the complex environment of a real pig barn.