文章摘要
韩雪,慕昱,盛桂敏.CCPSO优化支持向量机的鸟声识别技术研究[J].声学技术,2023,42(1):118~126
CCPSO优化支持向量机的鸟声识别技术研究
Research on the CCPSO optimized SVM based bird sound recognition technology
投稿时间:2021-07-10  修订日期:2021-08-05
DOI:10.16300/j.cnki.1000-3630.2023.01.020
中文关键词: 鸟声识别  音节分割  时频特征提取  支持向量机  混沌云粒子群优化(CCPSO)
英文关键词: bird recognition  syllable segmentation  time frequency feature extraction  support vector machine (SVM)  chaos cloud particle swarm optimization (CCPSO)
基金项目:黑龙江省教育厅基本科研业务费(2017-KYYWF-0723)。
作者单位E-mail
韩雪 黑龙江大学物理科学与技术学院, 黑龙江哈尔滨 150080
华南理工大学物理与光电学院, 广东广州 510640 
 
慕昱 黑龙江大学物理科学与技术学院, 黑龙江哈尔滨 150080 muyufffff@126.com 
盛桂敏 绥化学院电气工程学院, 黑龙江绥化 152061  
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中文摘要:
      鸟类是生态系统中的重要组成部分,鸟类物种的多样性对生态环境有重要作用。所以,通过鸟声信号来识别鸟类从而对其进行保护有现实意义。文章对鸟声信号采用双参数的双门限法进行分段,从鸟声信号中寻找出声音的起始点和终止点的具体帧,进一步进行特征提取,提取每段鸟声信号中的短时能量和短时平均幅度,短时语谱图中的平均值、对比度、熵,共5种特征,采用优化参数的支持向量机进行鸟类物种分类。结果表明,基于混沌云粒子群优化(Chaos Cloud Particle Swarm Optimization,CCPSO)的支持向量机对比普通支持向量机的分类准确度得到提升,可有效地识别鸟类。利用该方法实现鸟类物种保护和生态系统管理的目的。
英文摘要:
      Birds are an important member of the ecosystem and the diversity of bird species has a self-evident effect on the ecological environment. Therefore, it is of great practical significance to protection of birds by identifying them through the sound of bird calls. In this paper, the bird sound signal is segmented by double-gate detection method of double parameters and used to find the specific frame of the sound starting point and ending point. A total of 5 features such as the short-term energy and short-term average amplitude in each segment of bird sound signal and the average value, contrast, and entropy in the short-time spectrogram are extracted, and then the support vector machine (SVM) with optimized parameters is used for birds species classification. The results show that compared with the ordinary support vector machine, the classification accuracy based on the support vector machine optimized by chaos cloud particle swarm optimization (CCPSO) is greatly improved. This method can effectively identify the birds and achieves the purposes of bird species protection, scientific research and ecosystem management.
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