Neural networks may help farmers make sure their animals are happy.
What’s new: Researchers led by Elodie Briefer and Ciara Sypherd at University of Copenhagen developed a system that interprets the moods behind a pig’s grunts and squeals.
How it works: The authors trained convolutional neural networks to classify porcine expressions using a database of 7,414 vocal sounds made by animals engaged in 19 situations like feeding, fighting, running, or being led to a slaughterhouse.
- Experts in animal behavior classified each call’s sentiment as positive or negative using the situations as guides. For example, noises recorded while an animal was feeding or being reunited with a familiar snout were labeled positive. Those recorded during a fight or in a slaughterhouse were labeled negative.
- The authors trained two ResNet-50s on spectrograms of the calls. One network classified calls as positive or negative while the other labeled the situation.
Results: The models achieved 91.5 percent accuracy classifying the sentiment of calls and 81.5 percent identifying the situation. A method that classified calls without machine learning achieved 61.7 percent and 19.5 percent respectively.
Behind the news: The noises an animal makes aren’t the only indication of its wellbeing, but they offer a window into its mental state.
- Earlier work used feed-forward and generalized regression neural networks to forecast feeding behavior and detect pneumonia in pigs.
- Researchers at several universities in South Korea developed a convolutional neural network that classified whether cows were hungry, in heat, or coughing based on their utterances.
- Such technology could help humans, too. Zoundream, a startup based in Basel and Barcelona, plans to market a translator that interprets infant cries as expressions of hunger, pain, gas, or needing a hug.
Why it matters: The authors plan to develop a tool that would monitor hogs’ behavior and anticipate their needs. Science has shown that animals are capable of complex emotions, prompting countries like Australia and the United Kingdom to pass laws that protect livestock welfare. Systems that evaluate animals’ emotional states could help farms stay in regulatory compliance and make better homes for the creatures in their care, as well as reassure consumers that their food was produced humanely.
We’re thinking: This work has awakened our interest in programming with EIEIO.