orcAI : A Machine Learning Tool to Detect and Classify Acoustic Signals of Killer Whales in Audio Recordings

Abstract

Acoustic monitoring is an essential tool for investigating animal communication and behavior when visual contact is limited, but the scalability of bioacoustic projects is often limited by time‐intensive manual auditing of focal signals. To address this bottleneck, we introduce orcAI—a novel deep learning framework for the automated detection and classification of a broad acoustic repertoire of killer whales ( Orcinus orca ), including vocalizations (e.g., pulsed calls, whistles) and incidental sounds (e.g., breathing, tail slaps). orcAI combines a ResNet‐based Convolutional Neural Network (ResNet‐CNN) with Long Short‐Term Memory (LSTM) layers to capture both spatial features and temporal context, enabling the model to classify signals and to accurately determine their temporal boundaries in spectrograms. Trained on a comprehensive dataset from herring‐feeding killer whales off Iceland, the framework was designed to be adaptable to other populations upon training with equivalent data. Our final model achieves up to 98.2% accuracy on test data and is delivered as an open‐source tool with an easy‐to‐use command‐line interface. By providing a ready‐to‐use model that processes raw audio and outputs annotations, orcAI serves as a useful tool for advancing the study of killer whale vocal behavior and, more broadly, for understanding marine mammal communication and ecology.

Bibliographic Information