|本期目录/Table of Contents|

[1]叶 蓁,孙海信*,颜佳泉,等.鲸类声信号的分类系统设计[J].厦门大学学报(自然科学版),2017,56(01):144-148.[doi:10.6043/j.issn.0438-0479.201603046]
 YE Zhen,SUN Haixin*,YAN Jiaquan,et al.Design of Classification System of Whales Sound Signal[J].Journal of Xiamen University(Natural Science),2017,56(01):144-148.[doi:10.6043/j.issn.0438-0479.201603046]
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
56卷
期数:
2017年01期
页码:
144-148
栏目:
研究简报
出版日期:
2017-01-23

文章信息/Info

Title:
Design of Classification System of Whales Sound Signal
文章编号:
0438-0479(2017)01-0144-05
作者:
叶 蓁12孙海信1*颜佳泉1陈清峰1齐 洁1
1.厦门大学 信息科学与技术学院,水声通信与海洋信息技术教育部重点实验室,福建 厦门 361005; 2.闽南师范大学物理与信息工程学院,福建 漳州 363000
Author(s):
YE Zhen12SUN Haixin1*YAN Jiaquan1CHEN Qingfeng1QI Jie1
1.Key Laboratory of Underwater Acoustic Communication and Marine Information Technology,College of Information Science and Engineering,Xiamen University,Xiamen 361005,China; 2.School of Physics and Information Engineering,Minnan Normal University,Zhangz
关键词:
压缩感知 水声信号 稀疏表示 特征提取 分类识别
Keywords:
compressed sensingunderwater acoustic signal sparse representation feature extraction classification
分类号:
TP 274.5
DOI:
10.6043/j.issn.0438-0479.201603046
文献标志码:
A
摘要:
针对传统的水声信号分类技术处理方法复杂、特征提取时间长以及特征量多等问题,提出了一种基于稀疏表示的分类系统,先利用正交匹配追踪法(OMP)算法提取与水声信号最为匹配的少数原子作为目标特征,再采用支持向量机(SVM)进行分类.对鲸类声信号进行仿真实验,实验结果表明,不仅提高了压缩效率和运算速度,而且识别率高,在水声信号的实时处理中具有较高的实用价值.
Abstract:
Traditional classification technologies of underwater acoustic signals are involved withissues such as the complicated processing method,the prolonged feature extraction,vast features and other problems.In this paper,we propose a novel method based on sparse representation classification.First,we extract a spot of atoms matched best with underwater acoustic signals as signal features utilizing the OMP algorithm.Second,we adopt SVM as our classifier.Through experimental evaluations,the effect of this method is shown to provide a significant improvement in compression efficiencies,computing speeds and recognition rates.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-03-31 录用日期:2016-06-03
基金项目:国家自然科学基金(61471309); 福建省自然科学基金(2013J01258)
*通信作者:hxsun@xum.edu.cn
更新日期/Last Update: 1900-01-01