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<p>Passive acoustic monitoring (PAM) is a key technology for studying marine mammal populations. PAM typically generates large volumes of data that contain signals from multiple overlapping sources. To extract meaningful information from these data, automated tools are required that can cope with multiple sources, missed detections, and false alarms. This paper presents the Multiple-Animal Model-Based Acoustic Tracking (MAMBAT) framework, which integrates model-based localization with Bayesian multi-target tracking to automatically track multiple sound sources using acoustic data from wide baseline arrays. MAMBAT leverages a “Track-before-Localize” strategy followed by a “Localize-then-Track” strategy that does not require detection, classification, or association steps. The framework’s effectiveness is demonstrated through application to real-world datasets that contain multiple sperm whales from two ocean basins. MAMBAT advances our ability to monitor marine mammal distribution, abundance, and behavior, with potential to provide valuable information for conservation and management efforts.</p>

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The MAMBAT framework for acoustic tracking of multiple animals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月13日

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  • Pina Gruden,
  • Eva-Marie Nosal,
  • E. Elizabeth Henderson

摘要

Passive acoustic monitoring (PAM) is a key technology for studying marine mammal populations. PAM typically generates large volumes of data that contain signals from multiple overlapping sources. To extract meaningful information from these data, automated tools are required that can cope with multiple sources, missed detections, and false alarms. This paper presents the Multiple-Animal Model-Based Acoustic Tracking (MAMBAT) framework, which integrates model-based localization with Bayesian multi-target tracking to automatically track multiple sound sources using acoustic data from wide baseline arrays. MAMBAT leverages a “Track-before-Localize” strategy followed by a “Localize-then-Track” strategy that does not require detection, classification, or association steps. The framework’s effectiveness is demonstrated through application to real-world datasets that contain multiple sperm whales from two ocean basins. MAMBAT advances our ability to monitor marine mammal distribution, abundance, and behavior, with potential to provide valuable information for conservation and management efforts.