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<p>The broad application of passive acoustic monitoring provides a critical data foundation for studying soundscape ecology, necessitating automated analysis methods to accurately extract ecological information from vast soundscape data. This review comprehensively and cohesively examines two predominant approaches in soundscape analysis: soundscape component recognition and acoustic indices methods. Focusing on machine learning (ML)-based analysis methods for bird diversity assessment over the past five years, this review surveys representative research within each category, outlining their respective strengths and limitations. This not only addresses the growing interest in this field but also identifies research gaps and poses key questions for future studies. The insights from this review are anticipated to significantly enhance the understanding of ML applications in soundscape analysis, guiding subsequent investigative efforts in this rapidly evolving discipline, and thereby better supporting long-term biodiversity monitoring and conservation initiatives.</p>

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Decoding nature’s melody: significance and challenges of machine learning in assessing bird diversity via soundscape analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月14日

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  • Jiangjian Xie,
  • Shanshan Xie,
  • Yang Liu,
  • Xin Jing,
  • Mengkun Zhu,
  • Linlin Xie,
  • Junguo Zhang,
  • Kun Qian,
  • Björn W. Schuller

摘要

The broad application of passive acoustic monitoring provides a critical data foundation for studying soundscape ecology, necessitating automated analysis methods to accurately extract ecological information from vast soundscape data. This review comprehensively and cohesively examines two predominant approaches in soundscape analysis: soundscape component recognition and acoustic indices methods. Focusing on machine learning (ML)-based analysis methods for bird diversity assessment over the past five years, this review surveys representative research within each category, outlining their respective strengths and limitations. This not only addresses the growing interest in this field but also identifies research gaps and poses key questions for future studies. The insights from this review are anticipated to significantly enhance the understanding of ML applications in soundscape analysis, guiding subsequent investigative efforts in this rapidly evolving discipline, and thereby better supporting long-term biodiversity monitoring and conservation initiatives.