基于深度学习的鸟声识别技术研究——以北京翠湖国家城市湿地公园为例  

Research on Bird Sound Recognition Technology Based on Deep Learning:Taking Beijing Cuihu National Urban Wetland Park as an Example

作　　者：王一宇 夏舫 刘松 彭涛 郭慧敏 鉴海防[1] WANG Yiyu;XIA Fang;LIU Song;PENG Tao;GUO Huimin;JIAN Haifang(Institute of Semiconductors,Chinese Academy of Sciences,Beijing,China,100083;School of Physics and Electronics,Shandong Normal University,Jinan,Shandong,China,250358;Haidian District Wetland and Wildlife Conservation and Management Center,Beijing,China,100094)

机构地区：[1]中国科学院半导体研究所,北京100083 [2]山东师范大学物理与电子科学学院,济南250358 [3]北京市海淀区湿地和野生动植物保护管理中心,北京100094

出　　处：《园林》2024年第4期19-26,共8页Landscape Architecture Academic Journal

摘　　要：鸟类是生态系统的重要组成部分,在评估生态系统状态方面具有不可忽视的作用,因此鸟类的调查和监测对于保护生态环境和维护生物多样性至关重要。传统的鸟类监测手段需要消耗大量人力物力,并且结果的准确性和可靠性有限。针对这一问题,提出一种基于深度学习的鸟类声音数据分析识别系统,并在北京翠湖国家城市湿地公园进行应用示范,以验证系统的性能和准确性。该系统首先使用传统声音信号处理方法对前端拾音器采集的音频进行预处理,然后使用残差神经网络提取音频特征并进行分类,从而实现对目标声音所包含物种信息的自动识别。在系统运行期间,成功监测到有效鸟类声音片段共计200044条,其识别准确率达到93%。系统共识别出野生鸟类9目16科52种,其中,属于国家Ⅱ级重点保护野生动物有6种,分别是大天鹅、鸿雁、红角鸮、纵纹腹小鸮、蓝喉歌鸲、游隼;属于北京市重点保护野生动物共计22种,分别是苍鹭、夜鹭、普通鸬鹚、绿头鸭等。监测到音频片段中相对多度较高的前5个物种依次是苍鹭(26%)、鸿雁(16%)、夜鹭(13%)、白头鹎(11%)、普通鸬鹚(8%)。实验结果表明,该系统实现了对鸟类声音的自动采集和分析,显著提高了鸟类监测的效率,为风景园林的合理规划、生态节点的保留以及景观可持续性的提升提供了有力支持。Birds are an important part of the ecosystem and play an indispensable role in assessing the state of the ecosystem,so the survey and monitoring of birds are crucial for protecting the ecosystem and preserving biodiversity.Traditional means of bird monitoring consume a lot of workforce and resources,and the accuracy and reliability of the results could be improved.To address this problem,a deep learning-based bird sound data analysis and recognition system is proposed,and an application demonstration is carried out in the Cuihu National Urban Wetland Park in Beijing to verify the performance and accuracy of the system.Using traditional sound signal processing methods,the system fi rst preprocesses the audio captured by the front-end pickups.Then,it extracts audio features and classifi es them using residual neural networks to achieve automatic identifi cation of the species’information contained in the target sounds.During the system’s operation,200044 effective bird sound clips were successfully monitored,and its recognition accuracy reached 93%.The system identifi ed a total of 52 species of wild birds in 9 orders and 16 families,among which there are 6 species of national class II wildlife under key protection,namely,Cygnus cygnus,Anser cygnoides,Otus sunia,Athene noctua,Luscinia svecica,and Falco peregrinus;and a total of 22 species of Beijing’s key wildlife under key protection,namely,Ardea cinerea,Nycticorax nycticorax,Phalacrocorax carbo,and Anas platyrhynchos,etc.The system was able to automatically identify the species of wild birds in 9 orders and 16 families.The top 5 species with a higher relative plurality in the monitored audio clips are Ardea cinerea(26%),Anser cygnoides(16%),Nycticorax nycticorax(13%),Pycnonotus sinensis(11%),and Phalacrocorax carbo(8%),in that order.The experimental results show that the system achieves automatic collection and analysis of bird sounds,signifi cantly improves the effi ciency of bird monitoring,and provides strong support for the rational planning of landscap

关 键 词：翠湖国家城市湿地公园 鸟类监测 深度学习 鸟类声音识别 鸟类多样性 北京 

分 类 号：TU986[建筑科学—城市规划与设计]