文章摘要
王宸,陶毅,吴剑明.中华白海豚回声定位信号自动识别[J].声学技术,2020,39(4):425~429
中华白海豚回声定位信号自动识别
Automatic recognition of Sousachinensis echolocation signal
投稿时间:2019-05-04  修订日期:2019-06-18
DOI:10.16300/j.cnki.1000-3630.2020.04.007
中文关键词: 中华白海豚  回声定位信号  机器学习  种类识别
英文关键词: Sousachinensis  echolocation signal (click)  machine learning  species identification
基金项目:国家自然科学基金(41676024、41276039)资助项目。
作者单位E-mail
王宸 厦门大学海洋与地球学院, 福建省厦门 361102
厦门大学水声通信与海洋信息技术教育部重点实验室, 福建厦门 361102 
 
陶毅 厦门大学海洋与地球学院, 福建省厦门 361102
厦门大学水声通信与海洋信息技术教育部重点实验室, 福建厦门 361102 
taoyi@xmu.edu.cn 
吴剑明 厦门大学海洋与地球学院, 福建省厦门 361102
厦门大学水声通信与海洋信息技术教育部重点实验室, 福建厦门 361102 
 
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中文摘要:
      中华白海豚是我国一级保护动物,以往研究表明不同海域中华白海豚的回声定位(click)信号存在差异。文章以回声定位信号为识别标准,使用三种机器学习方法(K近邻法,决策树-分类回归树法和朴素贝叶斯法)识别中华白海豚。首先使用Teager-Kaiser能源算子(Teager-Kaiser Energy Operator,TKEO)方法和Gabor滤波器进行中华白海豚click信号的自动检测,然后通过倒谱的方法提取click信号的特征,最后利用3种机器学习方法识别中华白海豚。实验数据采集于雷州湾的中华白海豚以及厦门湾的中华白海豚和宽吻海豚的发声信号。实验中将厦门湾中华白海豚和雷州湾中华白海豚设为第一组,进行同种海豚识别;将厦门海域中华白海豚和宽吻海豚设为第二组,进行不同种海豚识别,实验结果表明这两组实验的平均识别准确率分别可以达到98%和96%。
英文摘要:
      Sousachinensis is the country grade one protected animal in China, previous researches have proved that there are differences in the echolocation (click) signals of sousa chinensis in different sea areas. In this paper, three machine learning methods (K-Nearest Neighbor, Decision Trees-Classification and Regression Tree, Navie Bays) are used to identify Sousachinensis on the basis of echolocation signals. Firstly, the Teager-Kaiser Energy Operator (TKEO) method and Gabor filter are used to automatically detect the click signal of Sousachinensis, then the cepstrum operation is applied to the echolocation signal (click) for a better feature representation before using the three machine learning methods. Experimental researches are conducted on the signals colleted from the Sousachinensis in Leizhou Bay and the Sousachinensis and bottlenose dolphins in Xiamen sea area. In the experiment, the Sousachinensis in Xiamen sea area and the the Sousachinensis in Leizhou bay are set as the first group to indentify the same species of dolphin; and the the Sousachinensis and the bottlenose dolphin in Xiamen sea area are set as the second group to identify different species of dolphins. The results show that the average recognition accuracy of these two groups of experiments can reach 98% and 96%, respectively.
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