'It brings you closer to the natural world': the rise of the Merlin birdsong identifying app W hen Natasha Walter first became curious about the birds around her, she recorded their songs on her phone and arduously tried to match each song with online recordings. After a friend recommended Merlin Bird ID, a free app, she tried it in her London garden and was delighted to discover the birds she assumed were female blackbirds - "this is how bad a birder I was" - were actually song thrushes and mistle thrushes. "I'm obsessed with Merlin - it's wonderful and it's been a joy to me," says Walter, a writer and human rights activist. "This is what AI and machine-learning have been invented for. Merlin is having a moment. The app, developed by the Cornell Lab of Ornithology in New York, which listens for birdsong and identifies the species singing, has been downloaded 33m times, in 240 countries and territories around the world. Britain has the second highest total number of users - more than 1.5 million in 2024, an 88% increase from 2023. Every month, there has been a 30% increase in new users of the app, whose sound identification function was launched in 2021. Merlin has been trained to identify the songs of more than 1,300 species around the world, with more birds added twice a year. Different songs make distinct patterns on spectrograms and Merlin is trained to recognise these different shapes and attribute them to a species. For latecomers to birding, or those lacking a knowledgeable friend, the app has become their teacher. "My fear at first was I wouldn't actually learn because I'm outsourcing my understanding of birds to this app," says Walter. "But that hasn't come to pass.

Identifying sequences of syllables within birdsongs is key to tackling a wide array of challenges, including bird individual identification and better understanding of animal communication and sensory-motor learning. Recently, machine learning approaches have demonstrated great potential to alleviate the need for experts to label long audio recordings by hand. However, they still typically rely on the availability of labelled data for model training, restricting applicability to a few species and datasets. In this work, we build the first fully unsupervised algorithm to decompose birdsong recordings into sequences of syllables. We first detect syllable events, then cluster them to extract templates -- syllable representations -- before performing matching pursuit to decompose the recording as a sequence of syllables. We evaluate our automatic annotations against human labels on a dataset of Bengalese finch songs and find that our unsupervised method achieves high performance. We also demonstrate that our approach can distinguish individual birds within a species through their unique vocal signatures, for both Bengalese finches and another species, the great tit.

Birdsong is the world's finest music. Yet in New Zealand, where birds are regarded as taonga or precious things, a unique ecological conundrum exists. For an estimated 70 million years, New Zealand was isolated by water from the rest of the world, and its birds evolved free from predator mammals. As a result, many of New Zealand's birds, including the iconic kiwi, lost the ability to fly. Unfortunately, predators such as rats, possums, and stoats have gradually been introduced and are increasing.

The Cacophony Project's broad vision is to bring back New Zealand's native birds using the latest technology to monitor bird populations and humanely eliminate the introduced predators that are endangering them. The project started in our founder's backyard to measure the effectiveness of his efforts to protect the birds on his property. From this simple beginning, the project has quickly grown into a system that includes two edge devices, a cloud server, and automatic identification of animals using machine learning. The project has been completely open source from the beginning and sees regular contributions from a wide variety of volunteers. In New Zealand, our birds are our taonga, our precious things.

The fictional character of Dr. Dolittle has captured the imagination of millions of children with his ability to talk to animals – and now the idea of using technology to listen to and better understand animals is capturing the imagination of AI experts around the world. For example, AI language-analysis technology is being used to decode the sounds of bottlenose dolphins and compile a dictionary of dolphin language. This work is taking place on a global scale, across a vast variety of species. Researchers are using technology to gather data that could address some of the biggest environmental challenges of our time, including some with grants from Microsoft's AI for Earth program helping achieve their goals. Here is a snapshot of some of the projects underway – and what they hope to achieve.