生物多样性 ›› 2024, Vol. 32 ›› Issue (10): 24313. DOI: 10.17520/biods.2024313 cstr: 32101.14.biods.2024313
郭倩茸1, 段淑斐1,*()(
), 谢捷2(
), 董雪燕3, 肖治术4(
)
收稿日期:
2024-07-12
接受日期:
2024-09-27
出版日期:
2024-10-20
发布日期:
2024-12-09
通讯作者:
*E-mail: duanshufei@tyut.edu.cn
基金资助:
Qianrong Guo1, Shufei Duan1,*()(
), Jie Xie2(
), Xueyan Dong3, Zhishu Xiao4(
)
Received:
2024-07-12
Accepted:
2024-09-27
Online:
2024-10-20
Published:
2024-12-09
Contact:
*E-mail: duanshufei@tyut.edu.cn
Supported by:
摘要:
鸟声标注用于标记声音中的鸟类信息, 如种类、声音结构等, 是鸟类被动声学监测及相关声学数据分析、物种自动识别分类的重要基础。本文以鸟声标注为重点, 比较了人工标注、自动标注和半自动标注等常用方法的优势, 点明了各自在数据质量、标注一致性和标注效率等方面面临的挑战, 同时探讨了这些标注方法在被动声学监测中的应用进展, 提出了自动标注模型优化、跨地区数据集建立和半自动标注系统完善等未来发展方向。尽管目前自动标注方法取得了显著进展, 但鸟声标注仍面临冷启动问题, 亟需更大规模的跨地区数据集和高效的质量检测半自动标注系统, 以满足标注数量和质量的双重要求。本综述有助于帮助鸟声数据集创建者和标注者更好地理解现有标注技术及其潜在的发展趋势, 为大规模鸟类声学监测数据的高效物种自动识别提供技术支撑。
郭倩茸, 段淑斐, 谢捷, 董雪燕, 肖治术 (2024) 鸟声标注技术及其在被动声学监测中的应用. 生物多样性, 32, 24313. DOI: 10.17520/biods.2024313.
Qianrong Guo, Shufei Duan, Jie Xie, Xueyan Dong, Zhishu Xiao (2024) Advances in bird sound annotation methods for passive acoustic monitoring. Biodiversity Science, 32, 24313. DOI: 10.17520/biods.2024313.
数据集类型 Dataset type | 优点 Advantage | 缺点 Disadvantage |
---|---|---|
网站收集的数据集 Datasets collected on the website | 扩大数据集, 研究个体的行为模式和活动范围、追踪物种的迁徙路径、监测不同物种的分布变化和评估地区的物种多样性 To expand datatsets, study individual behavioural patterns and ranges, track species migration pathways, monitor changes in the distribution of different species, and assess species diversity in an area | (1)数据收集主要集中在参与者密集的地区, 缺乏某些区域或鸟类的样本, 导致数据集在地理分布上的偏差 (1) Data collection is mainly focused on areas with a high concentration of participants, and there is a lack of samples for certain areas or birds, resulting in a bias in the geographical distribution of the dataset (2)数据采集依赖于公众, 参与者的专业水平参差不齐, 导致录音质量和准确性不一致 (2) Data collection relied on the public, and the level of expertise of participants varied, resulting in inconsistent recording quality and accuracy |
博物馆收集的数据集 Datasets collected by museums | 数据集质量高, 有助于深入研究物种的生理和行为特征、精准识别物种和对比不同时期的物种分布和种群变化等 The high-quality datasets collected are useful for in-depth study of the physiological and behavioral characteristics of species, accurate identification of species, and comparison of species distribution and population changes over time | 更新频率较低, 无法及时反映当前鸟类种群的变化和动态 The datasets are updated infrequently and do not reflect the changes and dynamics of current bird populations in a timely manner |
鸟类挑战赛公开数据集 Public datasets of bird challenge | 数据集质量高、标签完备、应用于小区域的特定物种监测和保护研究 The datasets are high-quality, well-labeled, and can be used for species-specific monitoring and conservation research in small areas | 集中于特定地理区域或物种研究, 导致数据集的代表性不足, 标签缺乏迁移性 The datasets focus on specific geographic regions or species studies, resulting in under-representation of the datasets and a lack of transferability of labels |
自建数据集 Self-managed datasets | 及时反映当前物种种群动态和变化, 补充未被广泛覆盖的小区域数据 The datasets reflect the current population dynamics and changes of species timely, and supplement the data of small regions that are not widely covered | 难以覆盖广泛的地理区域和多样的鸟类种类 The datasets are difficult to cover a wide geographical area and diverse bird species |
表1 不同类型鸟声数据集的比较
Table 1 Comparison of different types of bird sound datasets
数据集类型 Dataset type | 优点 Advantage | 缺点 Disadvantage |
---|---|---|
网站收集的数据集 Datasets collected on the website | 扩大数据集, 研究个体的行为模式和活动范围、追踪物种的迁徙路径、监测不同物种的分布变化和评估地区的物种多样性 To expand datatsets, study individual behavioural patterns and ranges, track species migration pathways, monitor changes in the distribution of different species, and assess species diversity in an area | (1)数据收集主要集中在参与者密集的地区, 缺乏某些区域或鸟类的样本, 导致数据集在地理分布上的偏差 (1) Data collection is mainly focused on areas with a high concentration of participants, and there is a lack of samples for certain areas or birds, resulting in a bias in the geographical distribution of the dataset (2)数据采集依赖于公众, 参与者的专业水平参差不齐, 导致录音质量和准确性不一致 (2) Data collection relied on the public, and the level of expertise of participants varied, resulting in inconsistent recording quality and accuracy |
博物馆收集的数据集 Datasets collected by museums | 数据集质量高, 有助于深入研究物种的生理和行为特征、精准识别物种和对比不同时期的物种分布和种群变化等 The high-quality datasets collected are useful for in-depth study of the physiological and behavioral characteristics of species, accurate identification of species, and comparison of species distribution and population changes over time | 更新频率较低, 无法及时反映当前鸟类种群的变化和动态 The datasets are updated infrequently and do not reflect the changes and dynamics of current bird populations in a timely manner |
鸟类挑战赛公开数据集 Public datasets of bird challenge | 数据集质量高、标签完备、应用于小区域的特定物种监测和保护研究 The datasets are high-quality, well-labeled, and can be used for species-specific monitoring and conservation research in small areas | 集中于特定地理区域或物种研究, 导致数据集的代表性不足, 标签缺乏迁移性 The datasets focus on specific geographic regions or species studies, resulting in under-representation of the datasets and a lack of transferability of labels |
自建数据集 Self-managed datasets | 及时反映当前物种种群动态和变化, 补充未被广泛覆盖的小区域数据 The datasets reflect the current population dynamics and changes of species timely, and supplement the data of small regions that are not widely covered | 难以覆盖广泛的地理区域和多样的鸟类种类 The datasets are difficult to cover a wide geographical area and diverse bird species |
标注技术 Annotation technique | 技术特征 Technical characteristic | 优点 Advantage | 缺点 Disadvantage | 针对缺点采取的措施 Measures taken in response to disadvantages | |
---|---|---|---|---|---|
人工标注 Manual annotation | 专家标注 Expert annotation | 完全依赖专家完成标注工作 Rely solely on experts to annotate | 准确率高 High accuracy | 时间成本高 High time cost | 提出众包标注 Proposing crowdsourced annotations |
公民科学 Citizen Science | 依赖爱好者完成标注工作 Rely on hobbyists to annotate | 效率高 High efficiency | 标签准确率不高 The accuracy of the label is not high | 标签质量控制: 提升数据标签质量、任务请求者细化任务内容、限制众包参与者 Label quality control measures: Improving the quality of data labeling, refining task content by task requesters, and limiting crowdsourcing participants | |
自动标注 Automatic annotation | 完全依赖模型完成标注工作 Rely entirely on the model for annotation | 效率高 High efficiency | 依赖模型性能 Depend on model performance | 优化模型、训练数据集专家标注 Optimizing the model and using expert-annotated datasets for training | |
半自动标注 Semi-automatic annotation | 标注工作依赖机器和人工 Rely on machines and humans | 效率高、准确率高 High efficiency and high accuracy | 需要人员和标签管理 Require personnel and label management | 信誉管理、任务分配、激励机制等方面优化管理 Optimizing management in terms of reputation management, task allocation, and incentive mechanism |
表2 标注技术的技术特征及优化措施
Table 2 Technical features and optimization measures of annotation techniques
标注技术 Annotation technique | 技术特征 Technical characteristic | 优点 Advantage | 缺点 Disadvantage | 针对缺点采取的措施 Measures taken in response to disadvantages | |
---|---|---|---|---|---|
人工标注 Manual annotation | 专家标注 Expert annotation | 完全依赖专家完成标注工作 Rely solely on experts to annotate | 准确率高 High accuracy | 时间成本高 High time cost | 提出众包标注 Proposing crowdsourced annotations |
公民科学 Citizen Science | 依赖爱好者完成标注工作 Rely on hobbyists to annotate | 效率高 High efficiency | 标签准确率不高 The accuracy of the label is not high | 标签质量控制: 提升数据标签质量、任务请求者细化任务内容、限制众包参与者 Label quality control measures: Improving the quality of data labeling, refining task content by task requesters, and limiting crowdsourcing participants | |
自动标注 Automatic annotation | 完全依赖模型完成标注工作 Rely entirely on the model for annotation | 效率高 High efficiency | 依赖模型性能 Depend on model performance | 优化模型、训练数据集专家标注 Optimizing the model and using expert-annotated datasets for training | |
半自动标注 Semi-automatic annotation | 标注工作依赖机器和人工 Rely on machines and humans | 效率高、准确率高 High efficiency and high accuracy | 需要人员和标签管理 Require personnel and label management | 信誉管理、任务分配、激励机制等方面优化管理 Optimizing management in terms of reputation management, task allocation, and incentive mechanism |
人工特征 Artificial feature | 特征提取方法 Feature extraction method | 参考文献 Reference |
---|---|---|
时域特征 Time domain feature | 短时能量 Short-term energy | |
短时平均幅度 Short-term average amplitude | ||
短时过零率 Short-term zero-crossing rate | Marin-Cudraz et al, | |
频域特征 Frequency domain feature | 基频 Fundamental frequency | |
子带能量比 Subband energy ratio | ||
梅尔频率倒谱系数 Mel frequency cepstrum coefficient | Chakraborty et al, | |
线性预测倒谱系数 Linear prediction cepstrum coefficient | Rabiner & Juang, | |
感知线性预测倒谱系数 Perceptual linear prediction cepstrum coefficient | Reynolds, | |
图像特征 Image feature | 图像频率统计 Image frequency statistics | Bastas et al, |
形状特征 Shape features | Lee et al, | |
纹理特征 Texture features | Ren et al, | |
边缘特征 Edge features | Kim & Kim, | |
深度学习特征 Deep learning features | Sevilla & Glotin, | |
时频特征 Time-frequency feature | 离散小波变换 Discrete wavelet transformation | Sun et al, |
小波包分解 Wavelet packet decomposition | Xie et al, | |
Gabor变换特征 Gabor transform features | Connor et al, | |
短时傅里叶变换 Short-time Fourier transformation | Mulimani & Koolagudi, | |
梅尔频率倒谱变换 Mel frequency cepstrum transformation | Usman et al, | |
Chirplet变换 Chirplet transformation | 谢将剑等, | |
匹配追踪 Matched pursuit | Stowell & Plumbley, | |
Gammatone听觉滤波器 Gammatone auditory filters | Stowell & Plumbley, |
表3 人工特征及其提取方法
Table 3 Artificial features and extraction methods
人工特征 Artificial feature | 特征提取方法 Feature extraction method | 参考文献 Reference |
---|---|---|
时域特征 Time domain feature | 短时能量 Short-term energy | |
短时平均幅度 Short-term average amplitude | ||
短时过零率 Short-term zero-crossing rate | Marin-Cudraz et al, | |
频域特征 Frequency domain feature | 基频 Fundamental frequency | |
子带能量比 Subband energy ratio | ||
梅尔频率倒谱系数 Mel frequency cepstrum coefficient | Chakraborty et al, | |
线性预测倒谱系数 Linear prediction cepstrum coefficient | Rabiner & Juang, | |
感知线性预测倒谱系数 Perceptual linear prediction cepstrum coefficient | Reynolds, | |
图像特征 Image feature | 图像频率统计 Image frequency statistics | Bastas et al, |
形状特征 Shape features | Lee et al, | |
纹理特征 Texture features | Ren et al, | |
边缘特征 Edge features | Kim & Kim, | |
深度学习特征 Deep learning features | Sevilla & Glotin, | |
时频特征 Time-frequency feature | 离散小波变换 Discrete wavelet transformation | Sun et al, |
小波包分解 Wavelet packet decomposition | Xie et al, | |
Gabor变换特征 Gabor transform features | Connor et al, | |
短时傅里叶变换 Short-time Fourier transformation | Mulimani & Koolagudi, | |
梅尔频率倒谱变换 Mel frequency cepstrum transformation | Usman et al, | |
Chirplet变换 Chirplet transformation | 谢将剑等, | |
匹配追踪 Matched pursuit | Stowell & Plumbley, | |
Gammatone听觉滤波器 Gammatone auditory filters | Stowell & Plumbley, |
软件名称 Software | 输入 Input | 模型 Model | 免费 Free | 网址 Website |
---|---|---|---|---|
Kaleidoscope Pro | 音节 Syllable | 隐马尔柯夫模型、K-means聚类算法 Hidden Markov model, K-means clustering algorithm | 否 No | |
BirdNET | 3秒声谱图 3 s spectrogram | BirdNET | 是 Yes | |
Avisoft-SASLab Pro | 音节 Syllable | 轴平行阈值、线性判别分析 Axis parallel threshold, linear discriminant analysis | 否 No | |
Arbimon | 音节 Syllable | 模板匹配 Template matching | 是 Yes | |
AviaNZ | 手动设置声谱图长度 Manually set the spectrogram length | 小波识别器 Wavelet detector | 是 Yes | |
Luscinia | 音节 Syllable | 动态时间扭曲 Dynamic time warping | 是 Yes | |
ChirpOMatic | 12秒的语音片段 12 s voice clips | 机器学习 Machine learning | 否 No | |
Merlin Bird ID | 深度学习 Deep learning | 是 Yes | ||
Shiny PNW-Cnet | 12秒的语音片段 12 s voice clips | PNW-Cnet | 是 Yes | |
Raven Pro | 音节 Syllable | 否 No |
表4 鸟声自动识别软件
Table 4 Automatic bird voice recognition software
软件名称 Software | 输入 Input | 模型 Model | 免费 Free | 网址 Website |
---|---|---|---|---|
Kaleidoscope Pro | 音节 Syllable | 隐马尔柯夫模型、K-means聚类算法 Hidden Markov model, K-means clustering algorithm | 否 No | |
BirdNET | 3秒声谱图 3 s spectrogram | BirdNET | 是 Yes | |
Avisoft-SASLab Pro | 音节 Syllable | 轴平行阈值、线性判别分析 Axis parallel threshold, linear discriminant analysis | 否 No | |
Arbimon | 音节 Syllable | 模板匹配 Template matching | 是 Yes | |
AviaNZ | 手动设置声谱图长度 Manually set the spectrogram length | 小波识别器 Wavelet detector | 是 Yes | |
Luscinia | 音节 Syllable | 动态时间扭曲 Dynamic time warping | 是 Yes | |
ChirpOMatic | 12秒的语音片段 12 s voice clips | 机器学习 Machine learning | 否 No | |
Merlin Bird ID | 深度学习 Deep learning | 是 Yes | ||
Shiny PNW-Cnet | 12秒的语音片段 12 s voice clips | PNW-Cnet | 是 Yes | |
Raven Pro | 音节 Syllable | 否 No |
标注技术 Annotation technology | 适用场景 Applicable scenarios |
---|---|
人工标注 Manual annotation | 标注小型数据集、发布基准数据集、标注新物种或不常见的物种、行为生态学研究和种间关系研究 Labeling small datasets, publishing benchmark datasets, labeling new or uncommon species, behavioral ecology studies, and interspecific relationship studies |
半自动标注 Semi-automatic annotation | 标注中型数据集 Labeling medium-sized datasets |
自动标注 Automatic annotation | 标注大型数据集、长期的被动声学监测、实时监控系统、环境变化评估, 以及大规模研究项目的初步筛选 Labeling large datasets, long-term passive acoustic monitoring, real-time monitoring systems, environmental change assessments, and initial screening of large-scale research projects |
表5 鸟声标注技术及适用场景对比
Table 5 Comparison of bird voice annotation technology and applicable scenarios
标注技术 Annotation technology | 适用场景 Applicable scenarios |
---|---|
人工标注 Manual annotation | 标注小型数据集、发布基准数据集、标注新物种或不常见的物种、行为生态学研究和种间关系研究 Labeling small datasets, publishing benchmark datasets, labeling new or uncommon species, behavioral ecology studies, and interspecific relationship studies |
半自动标注 Semi-automatic annotation | 标注中型数据集 Labeling medium-sized datasets |
自动标注 Automatic annotation | 标注大型数据集、长期的被动声学监测、实时监控系统、环境变化评估, 以及大规模研究项目的初步筛选 Labeling large datasets, long-term passive acoustic monitoring, real-time monitoring systems, environmental change assessments, and initial screening of large-scale research projects |
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