Introduction

In order to protect animals that live in remote environments, researchers must be able to find them to understand the movements of their populations over time. As long-term passive acoustic monitoring capabilities have grown more technologically sophisticated, automatic animal species identification tools built on large datasets from these recorded soundscapes have become an increasingly vital tool for conservation and ecological research. While models such as Google Perch have emerged that can classify thousands of bird vocalizations, similar models that can classify vocalizations from several whale species at once have proven more challenging to develop. The acoustic range of whale species is incredibly broad, ranging from as low as 10 Hz for blue whales to above 120kHz for odontocetes (toothed whales), and recordings also vary dramatically by location and with time, which can make model development difficult. Additionally, researchers often don’t know what types of vocalizations are made by some especially elusive whale species, which complicates identifying those animals in the soundscapes. This is illustrated in the decades-old mystery surrounding a sound, called a “Biotwang”, that was first recorded almost a decade ago in the depths of the Mariana Trench. The sound has a "metallic" or "chime-like" quality, quite unlike the tonal moans more typical of whale vocalizations. In a recent paper, our collaborators at the U.S. National Oceanic and Atmospheric Administration (NOAA) determined that the Biotwang sound is uniquely produced by the elusive Bryde’s whales (pronounced "broodus"). Today we are delighted to share Google’s latest whale bioacoustics model, which can identify eight distinct species, as well as multiple calls for two of those species. Following on our collaborator’s discovery connecting Biotwangs to the Bryde’s whale and in the same paper, we expanded the model to include Biotwangs and used it to label more than 200,000 hours of underwater recordings. Here we describe the model and discuss some of the new insights into the ecology of whale species it is helping researchers to unlock. The model is now available for download via Kaggle Models. This multi-label audio classification model, when applied to a 5-second context window of 24kHz mono audio, outputs 12 scores representing confidence that the audio contains a whale vocalization from a particular species (7 classes) or of a particular type (5 classes).

Training

Authors

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Ann N. Allen, Matt Harvey, Lauren Harrell, Megan Wood, Angela R. Szesciorka, Jennifer L. K. McCullough, Erin M. Oleson