基于PSO-PF算法的SVM识别方法及其在异常声音中的应用

doi:10.13190/j.jbupt.2018-246

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (3): 58-63.doi: 10.13190/j.jbupt.2018-246

基于PSO-PF算法的SVM识别方法及其在异常声音中的应用

韦娟¹, 张芃楠¹, 岳凤丽¹, 宁方立^2,3

1. 西安电子科技大学通信工程学院, 西安 710071;
2. 西北工业大学机电学院, 西安 710072;
3. 东莞市三航军民融合创新研究院, 广东东莞 523808

收稿日期:2018-10-17 出版日期:2019-06-28 发布日期:2019-06-20
作者简介:韦娟(1973-),女,副教授,E-mail:weijuan@xidian.edu.cn.
基金资助:
国家自然科学基金项目（51375385，51675425）；2018年东莞市社会科技发展（重点）项目（20185071021600）；陕西省重点研发计划项目（2018SF-365，2018GY-181）

Recognition and Application of Abnormal Sound Via SVM Based on PSO-PF

WEI Juan¹, ZHANG Peng-nan¹, YUE Feng-li¹, NING Fang-li^2,3

1. School of Telecommunications Engineering, Xidian University, Xi'an 710071, China;
2. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
3. Dongguan Sanhang Civil-military Integration Innovation Institute, Guangdong Dongguan 523808, China

Received:2018-10-17 Online:2019-06-28 Published:2019-06-20

摘要/Abstract

摘要： 针对异常声音识别率低和算法复杂度高等技术难题，提出了一种基于粒子群优化粒子滤波（PSO-PF）算法优化支持向量机（SVM）的识别方法.将PSO算法引入粒子滤波中，通过不断更新粒子速度和位置，使粒子群向高似然后验概率区域移动，提高粒子滤波的参数估计精度.将PSO-PF算法应用于SVM参数优化中，可解决现有SVM参数优化算法易陷入局部最优值等问题.实验结果表明，将所提方法应用于多类异常声音识别，能够有效提高识别率，降低算法复杂度.

关键词: 异常声音, 支持向量机, 粒子滤波, 粒子群优化, 参数优化

Abstract: In order to solve the problems of low recognition accuracy and high computation complexity in abnormal sound signals, a particle filter based on particle swarm optimization (PSO-PF) algorithm is proposed to optimize the parameters of support vector machine (SVM). To improve the estimation precision of particle filter, the particle swarm optimization is applied to drive all the particles to the regions in which their likelihoods are high, by updating the velocity and position of particles constantly. And the algorithm can avoid falling into local optimum in SVM parameter optimization. The experimental results show that the new algorithm can achieve higher recognition accuracy and lower computation complexity for abnormal sounds recognition.

Key words: abnormal sound, support vector machine, particle filter, particle swarm optimization, parameter optimization

中图分类号:

TN929.53

韦娟, 张芃楠, 岳凤丽, 宁方立. 基于PSO-PF算法的SVM识别方法及其在异常声音中的应用[J]. 北京邮电大学学报, 2019, 42(3): 58-63.

WEI Juan, ZHANG Peng-nan, YUE Feng-li, NING Fang-li. Recognition and Application of Abnormal Sound Via SVM Based on PSO-PF[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(3): 58-63.

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基于PSO-PF算法的SVM识别方法及其在异常声音中的应用

Recognition and Application of Abnormal Sound Via SVM Based on PSO-PF

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