Pillar 2 · Methods
What changed under the hood, and why crows are the right test species.
For fifty years, bioacoustics ran on hand-labeled spectrograms, a handful of named call types per species, and the slow grind of a graduate student's ear. Self-supervised audio models, latent-space analysis, and foundation models like NatureLM-audio changed the floor of the field around 2022–2025.
The leverage for crows specifically: a single species with a dense, graded repertoire, well-documented individual recognition, and decades of behavioral context to compare against. Methods that look like party tricks elsewhere become load-bearing here.
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Self-supervised audio
How a model learns without labels — masked prediction on millions of audio clips, the trick that made bioacoustics tractable at scale.
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Latent space 101
What an embedding is, what a latent space is, why dimensionality reduction is necessary, what UMAP does — and what it doesn't.
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NatureLM-audio
Earth Species Project's audio-language foundation model. Audio in, natural language out. ICLR 2025; SOTA on BEANS-Zero.
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Traditional vs ALP
The fifty-year hand-labeling regime versus the new map-based regime. What the field gained, what it gave up.