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 (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 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%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.
Personal wellbeing is greatly influenced by our childhood and adolescence, and the relationships formed during those phases of our development. The human-dog bond represents a significant relationship that started thousands of years ago. There is a higher prevalence of dog ownership around the world, especially in households including children. This has resulted in a growing number of researchers studying our interactions with dogs and an expanding evidence base from the exploration of child-dog interactions. We review the potential effects of child-dog interactions on the physical, mental, and social wellbeing of both species. A search of the SCOPUS database identified documents published between January 1980 and April 2022. Filtering for key inclusion criteria, duplicate removals, and inspecting the references of these documents for additional sources, we reviewed a total of 393 documents, 88% of which were scientific articles. We were able to define the numerous ways in which children and dogs interact, be it neutral (e.g., sharing a common area), positive (e.g., petting), or negative (e.g., biting). Then, we found evidence for an association between childhood interaction with dogs and an array of benefits such as increased physical activities, a reduction of stress, and the development of empathy. Nonetheless, several detrimental outcomes have also been identified for both humans and dogs. Children are the most at-risk population regarding dog bites and dog-borne zoonoses, which may lead to injuries/illness, a subsequent fear of dogs, or even death. Moreover, pet bereavement is generally inevitable when living with a canine companion and should not be trivialized. With a canine focus, children sometimes take part in caretaking behaviors toward them, such as feeding or going for walks. These represent opportunities for dogs to relieve themselves outside, but also to exercise and socialize. By contrast, a lack of physical activity can lead to the onset of obesity in both dogs and children. Dogs may present greater levels of stress when in the presence of children. Finally, the welfare of assistance, therapy, and free-roaming dogs who may interact with children remains underexplored. Overall, it appears that the benefits of child-dog interactions outweigh the risks for children but not for dogs; determination of the effects on both species, positive as well as negative, still requires further development. We call for longitudinal studies and cross-cultural research in the future to better understand the impact of child-dog interactions. Our review is important for people in and outside of the scientific community, to pediatricians, veterinarians, and current or future dog owners seeking to extend their knowledge, and to inform future research of scientists studying dogs and human-animal interactions.
The annual global production of chickens exceeds 25 billion birds, and they 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 annotations, which is very labour-intensive and time-consuming. Thus, a novel light-VGG11 was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on intensive chicken farms. The light-VGG11 was modified from VGG11 with a significantly smaller size in 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 further improve the 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%. In terms of precision livestock farming, our research opens new opportunities for developing technologies used to monitor the output of distress calls in large, commercial chicken flocks.
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