Scientists have developed an algorithm to monitor the underwater chatter of dolphins with the help of machine learning.
Using autonomous underwater sensors, researchers working in the Gulf of Mexico spent two years making recordings of dolphin echolocation clicks.
The result was a data set of 52 million click noises.
To sort through this vast amount of information, the scientists employed an “unsupervised” algorithm that automatically classified the noises into categories.
Without being “taught” to recognise patterns that were already known, the algorithm was able to seek original patterns in the data and identify types of click.
This enabled the scientists to determine specific patterns of clicks among the millions of clicks being recorded, and could help them to identify dolphin species in the wild.
Dr Frasier and her colleagues think their techniques could be employed to sift through large quantities of data and keep track of dolphin populations in a non-disruptive way.
Dolphins are an incredibly diverse family of mammals, and different species use different types of click to echolocate.
Submitted by: Arnfried Walbrecht
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