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ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning.

Christian Bergler1, Hendrik Schröter2, Rachael Xi Cheng3

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Deep neural networks efficiently identify killer whale vocalizations in large bioacoustic archives. ORCA-SPOT automates sound extraction, advancing the study of animal communication patterns.

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Area of Science:

  • Bioacoustics
  • Machine Learning
  • Animal Communication

Background:

  • Large bioacoustic archives are crucial for understanding animal communication but are often dominated by noise, hindering manual analysis.
  • Extracting vocalizations from noisy recordings is challenging, especially for species with complex vocal repertoires like killer whales (Orcinus orca).

Purpose of the Study:

  • To develop and evaluate a deep learning toolkit, ORCA-SPOT, for automated segmentation of killer whale sounds in large-scale bioacoustic datasets.
  • To enable efficient extraction of vocalizations for identifying communication patterns and advancing the study of intra-specific communication.

Main Methods:

  • Deep neural networks were trained on 11,509 killer whale signals and 34,848 noise segments.
  • The ORCA-SPOT toolkit was tested on the Orchive repository, containing approximately 19,000 hours of killer whale recordings.

Main Results:

  • Automated segmentation of 2.2 years of recordings was completed in approximately 8 days.
  • ORCA-SPOT achieved a time-based precision (positive-predictive-value) of 93.2% and an area-under-the-curve (AUC) of 0.9523.
  • The approach facilitates automated annotation of bioacoustic databases for killer whale sound extraction.

Conclusions:

  • ORCA-SPOT provides an effective automated method for analyzing large bioacoustic datasets, significantly improving the efficiency of killer whale sound extraction.
  • This deep learning approach can be adapted for other species, supporting broader applications in bioacoustic research and the study of animal communication.