Communications of the ACM
Bat Chat: Machine Learning Algorithms Provide Translations For Bat Squeaks
Researchers studying Egyptian fruit bats say they have found a way to work out who is arguing with whom, what they are squabbling about, and can even predict the outcome of a disagreement.
Credit: Michal Samuni-Blank
Researchers from Tel Aviv University in Israel are studying Egyptian fruit bats and have found a way to determine which bats are communicating with each other, what they are communicating about, and predict the outcome of a disagreement between bats.
The new approach involves harnessing machine-learning algorithms designed for human voice recognition. The researchers spent 75 days continuously recording audio and video footage of 22 bats that were divided into two groups and housed in separate cages. The researchers studied the video footage and were able to identify which bats were arguing with each other and the outcome of each disagreement. They divided each argument into one of our categories--sleep, food, perching position, and unwanted mating attempts, and then trained the algorithm with about 15,000 bat calls from seven adult females, each categorized using information taken from the video footage.
The researchers found that, based only on the frequencies within the bats' calls, the algorithm correctly identified the bat making the call about 71 percent of the time, and what the animals were fighting about around 61 percent of the time.
The system was also able to identify, with less accuracy, which bat the call was directed towards and predict the result of the disagreement.
From The Guardian
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