生物多样性 ›› 2026, Vol. 34 ›› Issue (2): 25296. DOI: 10.17520/biods.2025296 cstr: 32101.14.biods.2025296
谢将剑1,2(
), 朱梦坤1, 蒋爱伍3(), 肖治术4,*()(
)
收稿日期:2025-07-27
接受日期:2025-11-13
出版日期:2026-02-20
发布日期:2026-03-23
通讯作者:
E-mail: 基金资助:
Jiangjian Xie1,2(), Mengkun Zhu1, Aiwu Jiang3(), Zhishu Xiao4,*()()
Received:2025-07-27
Accepted:2025-11-13
Online:2026-02-20
Published:2026-03-23
Contact:
E-mail: Supported by:摘要：
随着全球生物多样性丧失风险的加剧, 发展高效、低成本且可持续的生态监测技术已成为生态保护与管理的重要需求。被动声学监测(passive acoustic monitoring, PAM)基于“声景”概念, 通过连续记录环境中的生物声、地理声和人工声, 为大尺度生物多样性长期监测提供了新的技术路径。近年来, 围绕声景数据的自动分析方法不断发展, 其中自动识别法与声学指数法已成为当前声景生物多样性自动评估的两种主流技术路线。本文系统综述了上述两类方法的基本原理、应用现状及其在实际生态监测应用中的主要局限。重点分析了自动识别法在标注数据稀缺、背景噪声与声音混叠干扰、跨区域泛化能力不足等方面遇到的技术瓶颈, 以及声学指数法在生态解释力有限、对环境与参数设置高度敏感、评估结果一致性不足等方面的问题。进一步从生物声多样性特征、环境干扰因素及数据采集策略等层面, 归纳了导致不同研究间评估结果不一致甚至相互矛盾的综合原因。在此基础上, 结合声学生态位假说与声学适应性假说, 提出未来声景生物多样性评估的发展方向, 包括数据采集与处理流程的标准化、声景特征数据库的共享与开放, 以及自动识别法与声学指数法的融合创新与多源信息整合。本文旨在为构建更准确、稳健和可推广的声景生物多样性自动评估体系提供系统参考。
谢将剑, 朱梦坤, 蒋爱伍, 肖治术 (2026) 聆听生物多样性的未来: 声景自动评估方法的局限性与发展方向. 生物多样性, 34, 25296. DOI: 10.17520/biods.2025296.
Jiangjian Xie, Mengkun Zhu, Aiwu Jiang, Zhishu Xiao (2026) Future of listening to biodiversity: Limitations and development directions of soundscape-based automatic assessment methods. Biodiversity Science, 34, 25296. DOI: 10.17520/biods.2025296.
| 评估方法 Evaluation method | 评估原理 Evaluation principle | 优点 Advantages | 缺点 Disadvantages |
|---|---|---|---|
| 自动识别法 Automatic recognition method | 利用机器学习或深度学习算法, 根据声源在频率与振幅随时间变化上的差异特征, 对录音中的声源进行自动分类或聚类。(1)分类是针对已知物种/声源, 建立模型并分配标签。(2)聚类是针对声景信号组成未知时, 仅完成声景信号的分组, 需后续人工鉴定分组的生态学含义。 Uses the machine learning or deep learning algorithms to automatically classify or cluster sound sources based on their distinctive patterns of frequency and amplitude variation over time. (1) Classification: building model to assign the labels for recordings containing a priori known species or sound sources. (2) Clustering: when the composition of the soundscape is unknown, it groups acoustically similar signals without prior labels, and the ecological meaning of each cluster requires subsequent expert interpretation. | (1)可直接获取详细的物种或声源信息, 有利于科研成果的公众传播。(2)在目标明确的场景中识别准确率高(Tang et al., (1) Enables direct acquisition of detailed information on species or sound sources, facilitating public dissemination of research outcomes. (2) Achieves high identification accuracy in target-specific scenarios (Tang et al., | (1)需要大规模标注数据或高质量训练集来保证算法性能(Sagar et al., (1) Requires large annotated datasets or high-quality training samples to ensure algorithm performance (Sagar et al., |
| 声学指数法 Acoustic index method | 利用数理统计方法, 在时间、振幅、频率等维度上提取一系列声学指数, 并将它们高度概括为单一或少数几个数值, 用以反映整个声景的群落结构信息。 Uses statistical methods to extract a series of acoustic indices across temporal, amplitude, and frequency dimensions, and summarizes them into one or a few numerical values to represent the community structure information of the entire soundscape. | (1)指标体系成熟, 计算相对简单, 效率高, 可快速得到结果(Bradfer-Lawrence et al., (1) Well-established index system; computationally simple, efficient, and capable of producing rapid results (Bradfer-Lawrence et al., | (1)对噪声、环境干扰敏感, 不同场景下指标含义不尽相同(Alcocer et al., (1) Sensitive to noise and environmental disturbances, the interpretation of indices varies across habitats (Alcocer et al., |
表1 基于声景的生物多样性自动评估方法的评估原理及其优缺点
Table 1 Principles, advantages, and limitations of soundscape-based automated biodiversity assessment methods
| 评估方法 Evaluation method | 评估原理 Evaluation principle | 优点 Advantages | 缺点 Disadvantages |
|---|---|---|---|
| 自动识别法 Automatic recognition method | 利用机器学习或深度学习算法, 根据声源在频率与振幅随时间变化上的差异特征, 对录音中的声源进行自动分类或聚类。(1)分类是针对已知物种/声源, 建立模型并分配标签。(2)聚类是针对声景信号组成未知时, 仅完成声景信号的分组, 需后续人工鉴定分组的生态学含义。 Uses the machine learning or deep learning algorithms to automatically classify or cluster sound sources based on their distinctive patterns of frequency and amplitude variation over time. (1) Classification: building model to assign the labels for recordings containing a priori known species or sound sources. (2) Clustering: when the composition of the soundscape is unknown, it groups acoustically similar signals without prior labels, and the ecological meaning of each cluster requires subsequent expert interpretation. | (1)可直接获取详细的物种或声源信息, 有利于科研成果的公众传播。(2)在目标明确的场景中识别准确率高(Tang et al., (1) Enables direct acquisition of detailed information on species or sound sources, facilitating public dissemination of research outcomes. (2) Achieves high identification accuracy in target-specific scenarios (Tang et al., | (1)需要大规模标注数据或高质量训练集来保证算法性能(Sagar et al., (1) Requires large annotated datasets or high-quality training samples to ensure algorithm performance (Sagar et al., |
| 声学指数法 Acoustic index method | 利用数理统计方法, 在时间、振幅、频率等维度上提取一系列声学指数, 并将它们高度概括为单一或少数几个数值, 用以反映整个声景的群落结构信息。 Uses statistical methods to extract a series of acoustic indices across temporal, amplitude, and frequency dimensions, and summarizes them into one or a few numerical values to represent the community structure information of the entire soundscape. | (1)指标体系成熟, 计算相对简单, 效率高, 可快速得到结果(Bradfer-Lawrence et al., (1) Well-established index system; computationally simple, efficient, and capable of producing rapid results (Bradfer-Lawrence et al., | (1)对噪声、环境干扰敏感, 不同场景下指标含义不尽相同(Alcocer et al., (1) Sensitive to noise and environmental disturbances, the interpretation of indices varies across habitats (Alcocer et al., |
| 挑战/局限性 Challenges/limitations | 自动识别法 Automatic recognition method | 声学指数法 Acoustic index method |
|---|---|---|
| 通用性/栖息地特异性 General applicability/ habitat specificity | 模型泛化能力差, 训练数据偏差导致跨区域适用性下降, 在不同声景中准确度显著降低。 Poor model generalization, training data bias reduces cross-regional applicability, leading to significant accuracy drops in different soundscapes. | 缺乏通用指数, 不同指数适用栖息地类型有限, 难以跨环境普适应用, 同一指数在不同生态系统可能指示不同生态意义。 Lack of universal indices, most indices are habitat-specific and not transferable across environments, with ecological meanings varying across ecosystems. |
| 对噪声的敏感性 Sensitivity to noise | 背景噪音和声音混叠严重降低识别精度, 即使有降噪技术也难以完全克服复杂环境噪声影响。 Severe background noise and overlapping sounds greatly reduce recognition accuracy, even with noise reduction techniques, complex environmental noise remains difficult to overcome. | 多数指数对背景噪声高度敏感, 需复杂降噪处理且可能丢失信息, 导致评估结果偏差。 Most indices are highly sensitive to background noise, requiring complex denoising that may cause information loss and biased evaluation results. |
| 与生物多样性关联 Correlation to biodiversity | 识别的是特定物种的存在或分类, 但物种间相似的声学特征和复杂生物学因素可能导致误识别。 Recognizes the presence or classification of specific species, but similar acoustic features and biological variability among species can cause misclassification. | 不能直接代表生物多样性, 多反映声音复杂度/丰度而非物种丰富度, 单一指数难以满足物种丰富度评估标准。 Does not directly represent biodiversity, mainly reflects acoustic complexity or abundance rather than species richness, and single indices cannot meet biodiversity assessment standards. |
| 数据需求与成本 Data requirements and costs | 严重依赖大规模、高质量标注音频数据集, 数据构建成本高昂, 且数据稀缺。 Heavily dependent on large, high-quality annotated datasets, data construction is costly and limited by data scarcity. | 计算效率高, 但指数有效性需针对性分析。 High computational efficiency, but the ecological validity of indices requires case-specific analysis. |
表2 自动识别法与声学指数法在实际应用中遇到的挑战与局限性对比
Table 2 Comparison of challenges and limitations encountered in practical applications of acoustic index and automatic recognition methods
| 挑战/局限性 Challenges/limitations | 自动识别法 Automatic recognition method | 声学指数法 Acoustic index method |
|---|---|---|
| 通用性/栖息地特异性 General applicability/ habitat specificity | 模型泛化能力差, 训练数据偏差导致跨区域适用性下降, 在不同声景中准确度显著降低。 Poor model generalization, training data bias reduces cross-regional applicability, leading to significant accuracy drops in different soundscapes. | 缺乏通用指数, 不同指数适用栖息地类型有限, 难以跨环境普适应用, 同一指数在不同生态系统可能指示不同生态意义。 Lack of universal indices, most indices are habitat-specific and not transferable across environments, with ecological meanings varying across ecosystems. |
| 对噪声的敏感性 Sensitivity to noise | 背景噪音和声音混叠严重降低识别精度, 即使有降噪技术也难以完全克服复杂环境噪声影响。 Severe background noise and overlapping sounds greatly reduce recognition accuracy, even with noise reduction techniques, complex environmental noise remains difficult to overcome. | 多数指数对背景噪声高度敏感, 需复杂降噪处理且可能丢失信息, 导致评估结果偏差。 Most indices are highly sensitive to background noise, requiring complex denoising that may cause information loss and biased evaluation results. |
| 与生物多样性关联 Correlation to biodiversity | 识别的是特定物种的存在或分类, 但物种间相似的声学特征和复杂生物学因素可能导致误识别。 Recognizes the presence or classification of specific species, but similar acoustic features and biological variability among species can cause misclassification. | 不能直接代表生物多样性, 多反映声音复杂度/丰度而非物种丰富度, 单一指数难以满足物种丰富度评估标准。 Does not directly represent biodiversity, mainly reflects acoustic complexity or abundance rather than species richness, and single indices cannot meet biodiversity assessment standards. |
| 数据需求与成本 Data requirements and costs | 严重依赖大规模、高质量标注音频数据集, 数据构建成本高昂, 且数据稀缺。 Heavily dependent on large, high-quality annotated datasets, data construction is costly and limited by data scarcity. | 计算效率高, 但指数有效性需针对性分析。 High computational efficiency, but the ecological validity of indices requires case-specific analysis. |
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