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.

翻译：现代家禽养殖的一个关注点是蛋鸡的叫声，这些叫声包含关于健康行为的非常有用的信息。这些信息被用作健康和福利指标，帮助饲养者更好地监测蛋鸡，从而能够早期发现问题以便快速有效地干预。在这项工作中，我们专注于通过声音分析识别蛋鸡叫声类型，以提出一个稳健的行为表征系统用于更优的监测。为此，我们首先收集并标注了蛋鸡的叫声信号，然后基于时域和频域特征的组合设计了一种最优的声学表征方法。接着，我们利用这些特征构建了基于循环神经网络的多标签分类模型，为表征蛋鸡行为的叫声分配语义类别。结果表明，基于时域和频域特征组合的模型整体性能最优，获得了最高F1分数（F1=92.75），相比仅使用频域特征的模型提升了17%，相比文献中的对比方法提升了8%。