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ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated

Christopher Hauer1, Elmar Nöth2, Alexander Barnhill2

  • 1Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany. Hauechri.Hauer@fau.de.

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This study introduces ORCA-SPY, a novel framework for tracking killer whales (Orcinus orca) using sound. It achieves high accuracy in localizing whale vocalizations, improving our understanding of marine mammal communication.

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Area of Science:

  • Marine bioacoustics and bioacoustics software development.
  • Computational bioacoustics and signal processing.
  • Wildlife monitoring and conservation technology.

Background:

  • Acoustic identification of individual animals is crucial for understanding communication but challenging underwater.
  • Lack of ground truth localization data hinders evaluation of passive acoustic monitoring (PAM) methods for marine species.
  • Killer whale (Orcinus orca) vocalizations are complex, requiring advanced techniques for individual identification and localization.

Purpose of the Study:

  • To present ORCA-SPY, a new automated framework for simulating, classifying, and localizing killer whale sounds.
  • To generate realistic, ground truth localization data for evaluating killer whale acoustic monitoring systems.
  • To provide an open-source tool adaptable for various recording conditions and marine species.

Main Methods:

  • Developed ORCA-SPY, a framework integrated into PAMGuard, featuring automated sound source simulation and classification.
  • Employed a hybrid approach combining ANIMAL-SPOT (a deep learning orca detector) with Time-Difference-Of-Arrival (TDOA) localization.
  • Evaluated the system using simulated multichannel audio streams and field tests in diverse underwater environments.

Main Results:

  • Achieved a 94.0% detection rate with an average localization error of 7.01 meters on simulated data across various conditions.
  • Field tests demonstrated practical performance with average localization errors of 29.19m (lab) and 20.01m (expedition).
  • The framework proved effective in real-world deployments, showing median errors as low as 11.01 meters.

Conclusions:

  • ORCA-SPY successfully simulates and localizes killer whale vocalizations with high accuracy, addressing the challenge of limited ground truth data.
  • The open-source framework offers a valuable tool for researchers studying marine mammal acoustics and behavior.
  • ORCA-SPY's adaptability suggests potential for broader applications in passive acoustic monitoring of other species.