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<p>Sound Event Localization and Detection (SELD) is a critical capability for environmental acoustic intelligence, enabling systems to jointly identify what sounds are active and where they originate. This technology is foundational for applications ranging from smart-city monitoring and autonomous systems to immersive media. Modern SELD research is dominated by deep learning approaches that leverage multi-channel audio, explicit spatial representations, and unified output formats that resolve complex and densely polyphonic scenes. This review provides a comprehensive synthesis of the field, charting its progress from foundational concepts to the state-of-the-art. We systematically analyze key methodological advancements, spatially-informed feature engineering, and the sophisticated data augmentation pipelines that underpin top-performing systems on public benchmarks. This review also highlights emerging opportunities for future research such as distance-aware 3-D SELD and the advancement of data-efficient learning paradigms. By translating these challenges into concrete research directions, this work aims to accelerate the progress of SELD toward robust, field-ready environmental intelligence tools.</p>

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Environmental acoustic intelligence through sound event localization and detection: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

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  • Jun-Wei Yeow,
  • Ee-Leng Tan,
  • Santi Peksi,
  • Woon-Seng Gan

摘要

Sound Event Localization and Detection (SELD) is a critical capability for environmental acoustic intelligence, enabling systems to jointly identify what sounds are active and where they originate. This technology is foundational for applications ranging from smart-city monitoring and autonomous systems to immersive media. Modern SELD research is dominated by deep learning approaches that leverage multi-channel audio, explicit spatial representations, and unified output formats that resolve complex and densely polyphonic scenes. This review provides a comprehensive synthesis of the field, charting its progress from foundational concepts to the state-of-the-art. We systematically analyze key methodological advancements, spatially-informed feature engineering, and the sophisticated data augmentation pipelines that underpin top-performing systems on public benchmarks. This review also highlights emerging opportunities for future research such as distance-aware 3-D SELD and the advancement of data-efficient learning paradigms. By translating these challenges into concrete research directions, this work aims to accelerate the progress of SELD toward robust, field-ready environmental intelligence tools.