How AI trained on birds is surfacing underwater mysteries

Underwater sound is critical for understanding the unseeable patterns of marine species and their environment. The ocean soundscape is full of mysterious noises and unfound discoveries. For example, the mysterious “biotwang” sound, recently attributed to the elusive Bryde’s whales by the U.S. National Oceanic and Atmospheric Administration (NOAA), illustrates the continuous challenge of new song types and species attributions being identified regularly.

Google has a long history of collaborating with external scientists on using bioacoustics for monitoring and protecting whales, including our original research models to detect humpback whale classifications and the release of our multi-species whale model in 2024. To keep up with this pace, Google’s approach to AI for bioacoustics is evolving to enable more efficient connections from new discoveries to scientific insights at scale. In August 2025, Google DeepMind released the latest Perch foundational bioacoustics model, Perch 2.0, a bioacoustics foundation model trained primarily on birds and other terrestrial vocalizing animals. Surprisingly, despite including no underwater audio in training, Perch 2.0 performed well as an embedding model for transfer learning in marine validation tasks.

In our latest paper, “Perch 2.0 transfers 'whale' to underwater tasks”, a collaboration between Google Research and Google DeepMind presented at the NeurIPS 2025 workshop on AI for Non-Human Animal Communications, we deep dive into these results. We show how this bioacoustics foundation model, trained mostly on bird data, can be used to enable and scale insights for underwater marine ecosystems, particularly for classifying whale vocalizations. We are also sharing an end-to-end tutorial in Google Colab for our agile modeling workflow, demonstrating how to use Perch 2.0 to create a custom classifier for whale vocalizations using the NOAA NCEI Passive Acoustic Data Archive through Google Cloud.