Trying to find a whale song in the ocean is like trying to find a needle in a haystack. But now, UNSW Sydney researchers say they've trained a model, with just a single case study, to find blue whale songs in recordings that span across decades and entire ocean basins.

In a new study, lead author UNSW Ph.D. candidate Ben Jancovich showed how a neural network--a deep learning model that can recognize patterns in data through interconnected layers of artificial neurons--could detect blue whale songs with remarkable accuracy.

The researchers' findings, published in the journal Scientific Reports, have implications for the field of ecology, paving a new way to analyze rare species across decades.

"Machine learning models traditionally need to be trained on thousands of recordings of the very whale song that they're trying to find," Jancovich says.

"However, this new model was trained on only one recording of a blue whale call."

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