Listening to the planet
Someone stood in the Amazon as the sun came up through the rain, in a Cairo market on a Ramadan night, beside a street vendor in Palestine calling out six pairs of socks for ten, and pressed record . Each point here is one of those moments. Two deep learning models have listened to each recording, and the sounds they hear as alike sit close together.
What am I looking at?
Which AI model is listening
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Soundscape
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Time of day
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What am I looking at?
When a deep learning model (AI) processes a recording, it turns the sound into a long list of numbers called an embedding . Recordings that sound alike end up with similar numbers.
Each list has hundreds of numbers, far too many to picture, so a mathematical transform called UMAP squeezes them into three dimensions while keeping similar sounds close together. Each point is one recording, placed by its embedding.
Different models process sounds differently. Switch between them and watch the points rearrange: one trained on animal sounds should group recordings by what is calling, one trained on everyday audio by their overall sound texture.
Embeddings matter beyond this map. Among other things, they let scientists build detectors for new species from only a handful of examples, and our research* found that embeddings learned from birdsong transfer remarkably well to other animal sounds.
* Ghani, Burooj; Denton, Tom; Kahl, Stefan; and Klinck, Holger. (2023). Global birdsong embeddings enable superior transfer learning for bioacoustic classification . Scientific Reports 13, 22876.
Recordings come from Freesound , each credited to its recordist. In the backdrop is Dal Lake in Kashmir, photographed by Suhail Skindar Sofi (Wikimedia Commons, CC BY-SA 4.0).
© 2026 Burooj Ghani