Automated identification of chicken distress vocalisations using deep learning models

Abstract
The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an “iceberg indicator” of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million vs 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e., precision (94.58%), recall (94.89%), F1-score (94.73%), and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11’s performance, we investigated the impacts of different data augmentation techniques (i.e., time masking, frequency masking, mixed spectrograms of the same class, and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Furthermore, a distress call detection demonstration on continuous audios exhibited our research’s potential for developing technologies to monitor the output of distress calls in large, commercial chicken flocks.

Methods
Recordings were collected in production facilities owned by Lengfeng Poultry Ltd., in Guangxi province, People’s Republic of China, between November and December 2017 and in November 2018. Chickens (mix of Chinese “spotted” and “three-yellow” breeds) were kept in stacked cages (three cages per stack, with 13-20 individuals per cage), with approximately 2,000 to 2,500 birds per house. The microphone was positioned approximately two meters up from the floor, mounted on the top cage of a stack in the middle of the barn. This placement was chosen to ensure that the recording devices did not interfere with the farm staff cleaning and maintaining the barns. All recordings were sampled at 22.05 kHz with a 16-bit bit-depth throughout the natural lifecycle (0 – 35 days) of chickens using a portable recorder (Zoom H4n Pro, Zoom Corporation, Tokyo, Japan). These recordings were collected from two different chicken flocks at the same farm, one of which was used as the development dataset to learn the model, and the other was used as a continuous testing dataset to verify the model’s generalisation capability.