Context-related variation in the peripartum vocalisations and phonatory behaviours of Holstein-Friesian dairy cows

Nonlinear vocal phenomena affect human perceptions of distress, size and dominance in puppy whines

While nonlinear phenomena (NLP) are widely reported in animal vocalizations, often causing perceptual harshness and roughness, their communicative function remains debated. Several hypotheses have been put forward: attention-grabbing, communication of distress, exaggeration of body size and dominance. Here, we use state-of-the-art sound synthesis to investigate how NLP affect the perception of puppy whines by human listeners. Listeners assessed the distress, size or dominance conveyed by synthetic puppy whines with manipulated NLP, including frequency jumps and varying proportions of subharmonics, sidebands and deterministic chaos. We found that the presence of chaos increased the puppy's perceived level of distress and that this effect held across a range of representative fundamental frequency (f_o) levels. Adding sidebands and subharmonics also increased perceived distress among listeners who have extensive caregiving experience with pre-weaned puppies (e.g. breeders, veterinarians). Finally, we found that whines with added chaos, subharmonics or sidebands were associated with larger and more dominant puppies, although these biases were attenuated in experienced caregivers. Together, our results show that nonlinear phenomena in puppy whines can convey rich information to human listeners and therefore may be crucial for offspring survival during breeding of a domesticated species.

Parental Behavior in Bovines

After approximately 9 months, cows give birth to one calf. They usually try to separate from the herd if kept outdoors, but this depends on the possibilities to hide. When the calf is born, the cow immediately stands up and starts licking the calf, and this is most intense for the first hour. During this time, the cow is very protective of her calf and may attack anyone trying to come close to it, especially if the cow is not so used to human handling. The calf tries to stand up and falls in the beginning, but it usually stands about half an hour later. Once standing, the calf searches for the udder, and after approximately 2 h, the calf is suckling the colostrum from the cow's teats. The calf is born without an immune defense, and it, therefore, needs to suckle enough colostrum from the cow to get protection against infections. If the calf does not succeed in suckling within 4-6 h after birth, the farmer must milk the cow and give the calf at least 2-4 l of colostrum in a teat bottle. If the farmer has frozen colostrum of good quality in the freezer, and it is either difficult to milk the cow or her colostrum is of too low quality, the frozen colostrum can be warmed up and given to the calf. Beef calves are usually allowed to stay with their mother until they are 6-8 months old, when they are separated from the cow and then weaned. Dairy calves are usually separated from their mother immediately or within a few days after birth and raised by artificial milk feeding. In the beginning or during the whole milk period (6-12 weeks), they may be kept individually indoors in crates or outdoors in hutches. After weaning, they are usually group housed.

Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.