Biodiv Sci ›› 2024, Vol. 32 ›› Issue (10): 24123. DOI: 10.17520/biods.2024123 cstr: 32101.14.biods.2024123
• Reviews • Previous Articles Next Articles
Zezhou Hao1(), Chengyun Zhang2(
), Le Li1(
), Bingtao Gao1(
), Wei Zeng3, Chun Wang1, Zixuan Wang2, Wantao Huang2, Yue Zhang2, Nancai Pei1,*(
)(
), Zhishu Xiao4(
)
Received:
2024-03-30
Accepted:
2024-08-14
Online:
2024-10-20
Published:
2024-09-08
Contact:
*E-mail: nancai.pei@gmail.com
Supported by:
Zezhou Hao, Chengyun Zhang, Le Li, Bingtao Gao, Wei Zeng, Chun Wang, Zixuan Wang, Wantao Huang, Yue Zhang, Nancai Pei, Zhishu Xiao. Applications of passive acoustic monitoring and evaluation in urban bird research[J]. Biodiv Sci, 2024, 32(10): 24123.
类型 Type | 名称 Name | 基本功能 Basic functions | 网址 Website |
---|---|---|---|
声学指标计算 Acoustic indices calculate | Scikit-maad | 基于Python平台 Based on the Python platform 提供预处理、声学指数和声压级计算模块 Providing preprocessing, acoustic indices and sound pressure level calculation modules 提供多样的音频处理工具集 Providing a diverse set of audio analysis utilities 支持大规模数据处理 Supporting large-scale data processing | |
seewave | 基于R语言平台 Based on the R language platform 支持音频信号处理和特征提取 Supporting audio signal processing and feature extraction 支持声学指数计算和可视化 Supporting the calculation and visualization of acoustic indices 并未针对大规模数据进行优化 Not optimized for large-scale data | ||
soundecology | 基于R语言平台 Based on the R language platform 支持声学指数计算 Supporting the calculation of acoustic indices 提供目标信息框选和栅格文件处理功能 Providing the functions of bounding box selection and raster file creation 支持大规模数据处理 Supporting large-scale data processing | ||
声场景分类 模型 Acoustic scene classification model | AlexNet | 深度卷积神经网络模型 Deep convolutional neural network model 以语谱图为输入特征 Using spectrogram as input feature 有效提取音频全局特征 Effectively extracting global audio features 适用于多种声音识别任务 Suitable for various sound recognition tasks | |
GoogleNet | 基于Inception的架构设计 Inception-based architecture design 采用1 × 1卷积核以优化效率 Utilizing 1 × 1 convolutional kernels for efficiency 适配语谱图输入进行音频分析 Adapting spectrogram inputs for audio analysis 轻量级网络, 计算需求低 Lightweight network with low computational demand | ||
CityNet | 基于卷积神经网络 Based on convolutional neural network CityBioNet: 专注于生物声音的识别 Specialized in recognizing biological sounds CityAnthroNet: 专门识别人为声音 Dedicated to identifying anthropogenic sounds 采用one-vs-one方法优化双模型性能 Using one-vs-one approach to enhance performance of dual models | ||
鸟鸣识别模型 Bird song recognition model | BirdNET | 基于CNN模型 Based on the CNN framework 利用大规模的已标记鸟类声音数据进行训练 Trained by using a large dataset of labeled bird sounds 能够处理多种音频格式的输入, 提高模型适用性 Capable of processing multiple audio formats, enhancing model adaptability 具备迁移学习能力 Equipped with transfer learning capabilities 提供了GUI界面, 便于非专业用户使用 Providing a GUI interface, making it user-friendly for non-experts | |
AudioMAE | 基于Transformer架构 Based on the transformer architecture 利用大规模本地无标签数据完成自监督预训练 Utilizing large-scale local unlabeled data for self-supervised pre-training 有标注数据量要求低 Requiring a low amount of labeled data 支持大规模的音频数据训练 Supporting training with large-scale audio data | ||
Google Perch | 基于CNN框架 Based on the CNN framework 在大量有标签生物声学数据上进行预训练 Pre-trained on a large amount of labeled bioacoustic data 对目标物种有标签数据的依赖小 Requiring minimal labeled data for the target species 具有迁移学习能力 Possessing transfer learning capabilities |
Table1 Common bird song data processing platforms
类型 Type | 名称 Name | 基本功能 Basic functions | 网址 Website |
---|---|---|---|
声学指标计算 Acoustic indices calculate | Scikit-maad | 基于Python平台 Based on the Python platform 提供预处理、声学指数和声压级计算模块 Providing preprocessing, acoustic indices and sound pressure level calculation modules 提供多样的音频处理工具集 Providing a diverse set of audio analysis utilities 支持大规模数据处理 Supporting large-scale data processing | |
seewave | 基于R语言平台 Based on the R language platform 支持音频信号处理和特征提取 Supporting audio signal processing and feature extraction 支持声学指数计算和可视化 Supporting the calculation and visualization of acoustic indices 并未针对大规模数据进行优化 Not optimized for large-scale data | ||
soundecology | 基于R语言平台 Based on the R language platform 支持声学指数计算 Supporting the calculation of acoustic indices 提供目标信息框选和栅格文件处理功能 Providing the functions of bounding box selection and raster file creation 支持大规模数据处理 Supporting large-scale data processing | ||
声场景分类 模型 Acoustic scene classification model | AlexNet | 深度卷积神经网络模型 Deep convolutional neural network model 以语谱图为输入特征 Using spectrogram as input feature 有效提取音频全局特征 Effectively extracting global audio features 适用于多种声音识别任务 Suitable for various sound recognition tasks | |
GoogleNet | 基于Inception的架构设计 Inception-based architecture design 采用1 × 1卷积核以优化效率 Utilizing 1 × 1 convolutional kernels for efficiency 适配语谱图输入进行音频分析 Adapting spectrogram inputs for audio analysis 轻量级网络, 计算需求低 Lightweight network with low computational demand | ||
CityNet | 基于卷积神经网络 Based on convolutional neural network CityBioNet: 专注于生物声音的识别 Specialized in recognizing biological sounds CityAnthroNet: 专门识别人为声音 Dedicated to identifying anthropogenic sounds 采用one-vs-one方法优化双模型性能 Using one-vs-one approach to enhance performance of dual models | ||
鸟鸣识别模型 Bird song recognition model | BirdNET | 基于CNN模型 Based on the CNN framework 利用大规模的已标记鸟类声音数据进行训练 Trained by using a large dataset of labeled bird sounds 能够处理多种音频格式的输入, 提高模型适用性 Capable of processing multiple audio formats, enhancing model adaptability 具备迁移学习能力 Equipped with transfer learning capabilities 提供了GUI界面, 便于非专业用户使用 Providing a GUI interface, making it user-friendly for non-experts | |
AudioMAE | 基于Transformer架构 Based on the transformer architecture 利用大规模本地无标签数据完成自监督预训练 Utilizing large-scale local unlabeled data for self-supervised pre-training 有标注数据量要求低 Requiring a low amount of labeled data 支持大规模的音频数据训练 Supporting training with large-scale audio data | ||
Google Perch | 基于CNN框架 Based on the CNN framework 在大量有标签生物声学数据上进行预训练 Pre-trained on a large amount of labeled bioacoustic data 对目标物种有标签数据的依赖小 Requiring minimal labeled data for the target species 具有迁移学习能力 Possessing transfer learning capabilities |
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