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Computers Identify Dog Characteristics from Barks

28 May 2015

SPAIN - Research from universities in Spain and Hungary have found that computer models can identify many pet characteristics through analysing the barks of dogs.

The research was done in the Computational Intelligence Group (CIG) from the School of Computing at Universidad Politécnica de Madrid (UPM), in collaboration with a veterinary student from Universidad Alfonso X El Sabio and the Department of Ethology from Eötvös Loránd University of Budapest.

Gender, age, context and individual recognition were identified with a high percentage of success through statistical and computational methods of pattern recognition applied to their barking.

These results could help organisations to identify the dog state to develop certain tasks.

Canine communication has been a research topic in the animal behaviour field over the last decade.

Most of the research has focused on studying how dogs are capable to understand different forms of human communication, for example by displaying gestures and human voice recognition.

This joint research aimed to understand the acoustic signals obtained from dog barking when they are subjected to certain situations.

The experiments were carried out in Budapest with eight dogs, three males and five females, of the Hungarian Mudi breed, which are usually used as sheep-dogs. Each dog (aged between one and 10) registered 100 barks.

A total of 800 barks was obtained by placing the dog in seven different situations: alone, the owner tied the dog to a tree; playing with a ball; fighting, the human pretended to attack the dog’s owner; receiving their food ration; in the company of a person who was foreign to the dog; and getting ready for going out with the owner.

Each one of the 800 barks was characterised from 29 acoustic measurements.

By using the diverse computational models obtained from the collected data during the experiment, researchers were able to successfully recognise the dog's gender the 85.13 per cent of the time while the age of the dog (recoded as young, adult and old) was classified without mistakes the 80.25 per cent of the time.

The task of identifying the situation in which the dog was it was successful the 55.50 per cent, while the recognition (among the eight dogs participating in the study) of the Mudi breed was successful the 67.63 per cent of the time.

This study reveals the biological relevance and richness of the information in dog barking and brings new possibilities in applied research.

For example, the assessment of dog behaviour is relevant for diverse organizations, therefore to develop a software programme which is able to identify fear, anxiety and levels of aggressiveness in a dog can be a big help.

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