Key Facts:
A UNSW-led study demonstrates how a new tool can detect blue whale calls with almost 100% accuracy, despite only being trained on one sample song. The tool has the potential to transform how scientists analyse rare and elusive species.
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 PhD candidate Ben Jancovich showed how a neural network – a deep learning model that can recognise patterns in data through interconnected layers of artificial neurons – could detect blue whale songs with remarkable accuracy.
The researchers' findings have implications for the field of ecology, paving a new way to analyse 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."
Because studies in ecology often require monitoring change across many years, he says the tool could help scientists unlock decades of recordings.
"When we're studying marine mammals, that data is often acoustic recordings – and when you've got really long recordings, finding all the individual animal calls is incredibly labour-intensive, slow and expensive," he says.
Manually analysing datasets that span decades, he says, "is just not possible for humans. And even with automation, it still might not be possible if we lack training data for the target species."
Jancovich says these limitations have prevented a "full exploitation of these long-term datasets."
He says to mine the wealth of information they contain, high-performance, cheap, accessible tools like the one he developed need to be made available and open source.
Turning one call into thousands
Traditionally, researchers comb through recordings, marking each call by hand. Automated methods have been developed, but the large, labelled training datasets they require simply don't exist for rare or hard-to-record species.
To solve the problem, Jancovich and the research team built an automated detector system – based on an existing system originally trained to detect human speech – capable of scanning vast audio archives for whale calls.
Deep learning was a powerful tool for this task, though tricky to implement.
"You need loads of training data and you need loads of compute power," Jancovich says. "That's a major limitation in some applications."
So, instead of collecting thousands of training recordings, the researchers used a single example of a blue whale song to generate a training dataset comprised of thousands of semi-synthetic songs.
The method worked by copying the original call and applying modifications such as pitch shifting, time stretching and embedding different types of background noise.
"These modifications are representative of natural variations in the animals' vocal behaviour," Jancovich says, "as well as what happens to sound as it propagates through the ocean."
When tested on real-world recordings, the detector trained on this data performed at a level comparable to detectors trained on far larger datasets.
For one pygmy blue whale population, it correctly detected 99.4% of calls.
"The surprising outcome is that a relatively simple data augmentation process enables really good performance from that one single training example," Jancovich says.
"You would think you'd need more data, more variation – but because these animals produce sounds that are so similar to one another, it works."
The result is a large, realistic dataset that can be used to train a detector.
Why blue whales?
The approach relies on the consistency of the target animal's vocalisations.
Blue whales, the largest animals on Earth, are difficult to study. They are endangered, widely dispersed and spend most of their lives underwater. But they also produce highly stereotyped calls – meaning individuals within the same population make almost identical sounds.
"For example, all the blue whales that live around Madagascar sing the same song, and all the ones near Antarctica sing a different song," Jancovich says.
This predictability makes it possible to model realistic variation from a single call, but it also means the new method is limited to animals that produce stereotyped calls.
"It wouldn't work for something like dolphins, where every individual has its own unique whistle."
A lighter footprint
Deep learning is a technique within the field of machine learning that uses deep neural networks, which are the foundation that AI models like ChatGPT are built on.
"Training large, deep neural networks can consume considerable amounts of electricity," says Jancovich. "So one of our aims was to do something that's very compute-efficient."
The result is a model that can be trained on a standard laptop in a matter of hours, rather than weeks.
"It's a smaller model – and we're fine-tuning something that's already been trained, so it doesn't need as much data, as much training time or compute power."
Unlocking decades of data
Around the world, vast archives of underwater recordings have been collected through passive acoustic monitoring – hydrophones left in the ocean to record continuously.
But without efficient detection tools, much of that data remains underused.
"The raw data is quite hard to exploit because researchers don't always have access to high-performance automated detection methods, or the necessary training data and compute resources," Jancovich says.
The next step for the research team is to apply the detector to a 25-year dataset from the central Indian Ocean, tracking long-term changes in blue whale song.
It may also open new windows into animal behaviour. Whale songs, for example, are not just signals.
"They help us study things like animal culture – the way animals learn songs from each other across generations," Jancovich says.
And the same technique could be applied to detecting and monitoring other species – from birds to insects – that produce consistent, repeatable calls. Ecologists already have microphones placed not just in the oceans, but in forests and other remote environments.
"If accurate detectors can be trained from a single good recording, this can help us study rare and elusive species that have seldom been heard by humans," says Jancovich.
