数据不平衡下鸟声识别的集成学习策略  

Ensemble learning strategy for birdsong recognition under data imbalance

作　　者：申小虎[1,2] 李冠宇 史洪飞[2] 王传之 Xiaohu Shen;Guanyu Li;Hongfei Shi;Chuanzhi Wang(Department of Forensic Science and Technology,Jiangsu Police Institute,Nanjing 210031,China;Key Laboratory of National Forest and Grassland Administration on Wildlife Evidence Technology,Nanjing 210023,China;College of Information Science and Technology,Dalian Maritime University,Dalian,Liaoning 116026,China;iFLYTEK CO.,LTD.,Hefei 230088,China)

机构地区：[1]江苏警官学院刑事科学技术系,南京210031 [2]野生动植物物证技术国家林业和草原局重点实验室,南京210023 [3]大连海事大学信息科学与技术学院,辽宁大连116026 [4]科大讯飞科技有限公司,合肥230088

出　　处：《生物多样性》2024年第10期114-128,共15页Biodiversity Science

基　　金：野生动植物物证技术国家林业和草原局重点实验室开放课题(KLNPC2102);江苏省交通安全设施智能网联工程研究中心平台资助。

摘　　要：鸟声识别是被动声学监测的重要应用领域,集成学习方法对提升鸟类识别精度具有重要研究价值,但面对数据不平衡问题时缺少有效的集成策略。为此,通过基学习器的迁移学习获得鸟声信号的不同方面表征,满足了少标签样本条件下的学习训练。同时,设计加入自注意力机制的特征融合和敏感正则项用于提升模型对稀有鸟类的关注度,确保集成模型在信息不对称情况下推理时获得全局最优解。本文在南京老山森林公园共收集了10种鸟类样本,并对预训练模型完成了微调。通过鸟声识别分类实验,在样本不平衡的自建数据集与BirdCLEF 2023数据集上,总体分类精度分别达到了95.29%和90.17%。本文所提出的集成学习策略提升了少量样本类别的敏感度,增强了模型的泛化能力和学习训练效率,与主流集成学习方法相比较,能更好地适用于当地稀有鸟类的被动鸟声监测与识别,助力鸟类生态环境的精准保护。Aim&Summary:The dynamics and distribution changes of bird populations are essential components of ecosystems and critical for maintaining ecological balance.Recently,the rapid development of acoustic monitoring technologies has enabled passive acoustic bird recognition to become an efficient and non-invasive method for bird monitoring.However,the collection and annotation of bird sound data face numerous challenges for practical application,particularly issues of data imbalance and sample scarcity,which severely limit the improvement of recognition accuracy.We focus on the application of ensemble learning methods in bird recognition to solve the issue of rare bird species identification under data imbalance conditions while enhancing the generalization ability and training efficiency of the model.Our study designs a cost-sensitive ensemble learning strategy to overcome the limitations posed by imbalanced and scarce bird sound data.Thus,we improve the recognition accuracy of rare bird species.We construct an efficient and accurate passive acoustic bird recognition system that provides strong support for the precise conservation of avian environments by integrating techniques such as transfer learning,self-attention mechanisms,and sensitive regularization terms.Methods:To achieve the aforementioned objectives,we propose an improved cost-sensitive stacking ensemble learning strategy(cost-sensitive stacking ensemble for bird sound recognition,CSE-BSR).The specific methods include:(1)preprocessing collected bird sound data,including noise reduction,feature extraction,and spectrogram analysis,to improve model performance and reduce training time;(2)selecting deep learning models pre-trained on large bird sound datasets as base learners and fine-tuning them through transfer learning to better adapt to new recognition tasks;(3)designing a feature fusion method based on self-attention mechanisms to effectively integrate homogeneous yet heterogeneous features output by base learners,enhancing feature representation and mod

关 键 词：鸟声识别 数据不平衡 集成学习 迁移学习 敏感代价 

分 类 号：Q958[生物学—动物学] TN912.34[电子电信—通信与信息系统]