Attention-Based Recurrent Neural Network For Automatic Behavior Laying
  Hen Recognition
Fréjus A. A. Laleye Mikaël A. Mousse
One of the interests of modern poultry farming is the vocalization of laying
hens which contain very useful information on health behavior. This information
is used as health and well-being indicators that help breeders better monitor
laying hens, which involves early detection of problems for rapid and more
effective intervention. In this work, we focus on the sound analysis for the
recognition of the types of calls of the laying hens in order to propose a
robust system of characterization of their behavior for a better monitoring. To
do this, we first collected and annotated laying hen call signals, then
designed an optimal acoustic characterization based on the combination of time
and frequency domain features. We then used these features to build the
multi-label classification models based on recurrent neural network to assign a
semantic class to the vocalization that characterize the laying hen behavior.
The results show an overall performance with our model based on the combination
of time and frequency domain features that obtained the highest F1-score
(F1=92.75) with a gain of 17% on the models using the frequency domain features
and of 8% on the compared approaches from the litterature.