orcAI: A Machine Learning Tool to Detect and Classify Acoustic Signals of Killer Whales in Audio Recordings
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Date
2025
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Journal Article
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yes
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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.
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published
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Wiley
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Subject
Bioacoustics; Cetaceans; Deep learning
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03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
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