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Orcinus orca (killer whales) exhibit complex calls. They last about a second.In a call, an orca typically uses multiple frequencies simultaneously, variesthe frequencies, and varies their volumes. Behavior data is hard to obtainbecause orcas live under water and travel quickly. Sound data is relativelyeasy to capture. As a science goal, we would like to know whether orcavocalizations constitute a semantic language. We do this by studying whethermachine learning can predict behavior from vocalizations. Such prediction wouldalso help scientific research and safety applications because one would like topredict behavior while only having to capture sound. A significant challenge inthis process is lack of labeled data. We work with recent recordings of McMurdoSound orcas [Wellard et al. 2020] where each recording is labeled with thebehaviors observed during the recording. This yields a dataset where soundsegments - continuous vocalizations that can be thought of as call sequences ormore general structures - within the recordings are labeled with superfluousbehaviors. Despite that, with a careful combination of recent machine learningtechniques, we achieve 96.4% classification accuracy. This suggests that orcasdo use a semantic language. It is also promising for research and applications.

Do Orcas Have Semantic Language? Machine Learning to Predict Orca Behaviors Using Partially Labeled Vocalization Data