This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Is a dog's bark simply an expression of emotion, or does it contain specific information that humans cannot understand? Artificial intelligence (AI) can already analyze thousands of animal sounds and identify patterns that would go unnoticed by the human ear.
But researchers are asking a far more ambitious question: Could technology one day help us understand what animals are actually saying? According to professor Rytis Maskeliūnas of the Faculty of Informatics at Kaunas University of Technology (KTU), this is already an established area of research, although we are still a long way from truly translating animal communication. Today, AI makes it possible to analyze vast amounts of audio data that would be impossible for a human to process and identify recurring signals and their links with animal behavior and the surrounding environment.
This opens the door to much more complex questions—not only what sound an animal makes, but also when it makes it, who it is directed at and under what circumstances. "However, determining what information an animal is communicating from sound alone is far from straightforward. Acoustic analysis needs to be combined with behavioral observations and controlled experiments so that we can test whether an identified pattern genuinely has a specific communicative function.
That is why I would place less emphasis on the idea of an imminent animal-language translator and more on the possibility of systematically uncovering, for the first time, what information is contained in animal communication," says Maskeliūnas. Maskeliūnas and his colleagues began investigating the information contained in animal sounds several years ago. The researchers wanted to determine how much could be learned about a dog's emotional state from its sounds alone.
For the study, the researchers collected recordings of sounds made by German shepherds and, based on assessments by animal behavior experts, analyzed four categories associated with the dogs—happiness, anger, crying and loneliness. "We did not analyze the dogs' sounds simply as audio recordings. We broke them down into different frequency and time components and extracted acoustic features from them.
We then used machine-learning methods to test whether these features could distinguish between different emotional states. The results showed that a considerable amount of information can be obtained from a dog's voice—we were able to identify angry barking with fairly high reliability," explains Maskeliūnas. According to Maskeliūnas, however, the most important conclusion of the study was not the ability to determine whether a dog was angry or lonely.
Extract — continue reading at the source.
