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Thursday, January 17, 2008 : 1600 Hrs

Sci. & Tech.
Computer learns dogspeak

Study shows computer programs can classify dog barks better than humansThe aim 
of Molnár and colleagues' experiments was to test a computer algorithm's
ability to identify and differentiate the acoustic features of dog barks, and 
classify them according to different contexts and individual dogs.

According to a press release by EurekAlert, the software analyzed more than 
6000 barks from 14 Hungarian sheepdogs (Mudi breed) in six different situations:
'stranger', 'fight', 'walk', 'alone', 'ball' and 'play'. The barks were 
recorded with a tape recorder before being transferred to the computer, where 
they
were digitalized and individual bark sounds were coded, classified and 
evaluated.

In the first experiment looking at classification of barks into different 
situations, the software correctly classified the barks in 43 percent of cases.
The best recognition rates were achieved for 'fight' and 'stranger' contexts, 
and the poorest rate was achieved when categorizing 'play' barks. These findings
suggest that the different motivational states of dogs in aggressive, friendly 
or submissive contexts may result in acoustically different barks.

In the second experiment looking at the recognition of individual dogs, the 
algorithm correctly classified the barks in 52 percent of cases. The software
could reliably discriminate among individual dogs while humans can not, which 
suggests that there are individual differences in barks of dogs even though
humans are not able to recognise them.

The authors conclude by highlighting the value of their new methodology: "The 
use of advanced machine learning algorithms to classify and analyze animal
sounds opens new perspectives for the understanding of animal communication.The 
promising results obtained strongly suggest that advanced machine learning
approaches deserve to be considered as a new relevant tool for ethology*."
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