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

doi:10.13190/j.jbupt.2018-246

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (3): 58-63.doi: 10.13190/j.jbupt.2018-246

• Papers • Previous Articles Next Articles

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

Abstract

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

CLC Number:

TN929.53

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.

References

[1] Dan S, Dimitrios G, Emmanouil B, et al.Detection and classification of acoustic scenes and events[J].IEEE Transactions on Multimedia, 2015, 17(10):1-1.
[2] 周长林, 钱志升, 王勤民, 等.基于PSO-SVM方法的电源线传导泄漏信号识别与还原[J].电子与信息学报, 2018, 40(9):2207-2211.Zhou Changlin, Qian Zhisheng, Wang Qinmin, et al.Identification of conductive leakage signal in power cable based on multi-classification PSO-SVM[J].Journal of Electronics and Information Technology, 2018, 40(9):2207-2211.
[3] Chapelle O, Vapnik V, Bousquet O, et al.Choosing multiple parameters for support vector machines[J].Machine Learning, 2002, 46(3):131-159.
[4] 翟世俊.基于UWB信号的目标识别关键技术研究[D].北京邮电大学, 2014.
[5] Li Ke, Wang Lingyu, Wu Jingjing, et al.Using GA-SVM for defect inspection of flip chips based on vibration signals[J].Microelectronics Reliability, 2018, 81(2):159-166.
[6] Wang Yingmin, Cui Tao, Zhang Fujun, et al.Fault diagnosis of diesel engine lubrication system based on PSO-SVM and centroid location algorithm[C]//International Conference on Control, Automation and Information Sciences(ICCAIS).New York:IEEE Press, 2017:221-226.
[7] Zhang Xiaoli, Chen Xuefeng, He Zhengjia.An ACO-based algorithm for parameter optimization of support vector machines[J].Expert Systems with Applications, 2010, 37(9):6618-6628.
[8] Wang Ershen, Jia Chaoying, Tong Gang, et al.Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm[J].Advances in Space Research, 2018, 61(5):1261-1271.
[9] Wang Xiangyang, Wang Ying, Wan Wanggen, et al.Object tracking with sparse representation and annealed particle filter[J].Signal Image and Video Processing, 2014, 8(6):1059-1068.
[10] Ji Chunlin, Zhang Yangyang, Tong Mengmeng, et al.Particle filter with swarm move for optimization[C]//International Conference on Parallel Problem Solving from Nature.Berlin:Springer, 2008:909-918.
[11] Chen Zhimin, Qu Yuanxin, Liu Bing, et al.Improved multiple model target tracking algorithm based on particle filter[J].Proceedings of the Institution of Mechanical Engineers, Part G:Journal of Aerospace Engineering, 2015, 230(4):747-759.
[12] Zhang Gongyuan, Cheng Yongmei, Yang Feng, et al.Particle filter based on PSO[C]//International Conference on Intelligent Computation Technology and Automation.New York:IEEE Press, 2008:121-124.
[13] Kennedy J, Eberhart R, et al.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks.Perth:IEEE Xplore, 1995:1942-1948.
[14] Zhao Zengshun, Feng Xiang, Lin Yanyan, et al.Improved Rao-Blackwellized particle filter by particle swarm optimization[J].Journal of applied mathematics, 2013(5):302170-1-302170-8.
[15] Wang Eershen, Li Rui, Pang Tao, et al.Research on GPS RAIM algorithm using PF based on PSO[C]//China Satellite Navigation Conference (CSNC) 2016 Proceedings.Berlin:Springer, 2016:199-210.
[16] Zhou Zhiyu, Zhang Ruoxi, Wang Yaming, et al.Color difference classification based on optimization support vector machine of improved grey wolf algorithm[J].Optik, 2018, 170:17-29.

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

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	ZHOU Yao, SUN Jun, LUO Huilong, DAI Shuhao. Design of Broadband Metamaterial Absorber Based on Topology Optimization [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 84-90.
[2]	TIAN Zhongda, PAN Xinpeng. Network Traffic Prediction Using Wavelet Denoising and Optimized Support Vector Machine [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(5): 79-84.
[3]	ZHENG Feng, YANG Li. Joint Beamforming Scheme Based on PSO Algorithm for RIS-MIMO System [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(5): 23-29.
[4]	LIU Yong-li, ZHU Ya-meng, CHAO Hao. Hyperparameter Optimization of Convolutional Neural Network Based on Multi-Strategy MRFO Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(6): 83-88,95.
[5]	LIU Zhan-feng, PAN Su. Fuzzy-Rough Bireducts Algorithm Based on Particle Swarm Optimization [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 49-55.
[6]	WEI Juan, GU Xing-quan, NING Fang-li. An Abnormal Sound Recognition Method Based on EEMD [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 112-117.
[7]	HUANG Yi-bin, YANG He, ZHOU Zhong-bing, LIU Xiao. An Optimization Method for Urban Underground Parking Lots Allocation Based on Polygon Decomposition [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(4): 7-14.
[8]	QIN Yun-hui, HUANGFU Wei, LONG Ke-ping. Quaternion-Based Particle Swarm Optimization Algorithms for Mobile Communication Network Coverage [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(4): 21-26.
[9]	YE Xiao-tong, LIU Zhou-bin, SHAO Su-jie, QI Feng. Dynamic Allocation Algorithm of WiFi Access Resources Based on the Game Theory [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(2): 10-15,58.
[10]	ZHAO Xiong-wen, SUN Ning-yao, GENG Sui-yan, ZHANG Yu, DU Fei. Time-Varying Channel Modeling Using Least Square Support Vector Machine [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(5): 29-35.
[11]	YANG Fu-xing, HU Zhi-chao, KONG Ji-li. PDVRPTF Based on Dynamic Clustering Hybrid Topological Structure Particle Swarm Optimization [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(1): 16-21.
[12]	TAO Yi-wen, LU Yang, AN Chun-yan, LI Bin, ZHAO Cheng-lin. Idle Channel Detection Scheme under Impulsive Noise Environments [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(1): 41-46.
[13]	WU Qing, QI Shao-wei, SUN Kai-yue, ZANG Bo-yan, ZHAO Xiang. Least Squares Large Margin Twin Support Vector Machine [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(6): 34-38.
[14]	LI Dong-jing, HAO Da-xian, WANG Qi-long, YUN Chao. Workspace Analysis and Layout Optimization of Multi-station Working Island type Polishing Robot [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(3): 92-100.
[15]	CHAO Hao, LU Bao-yun, LIU Yong-li, LIU Zhi-zhong, SONG Cheng. Vocal Effort Detection Based on Spectral Information Entropy and Complementary Models [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(2): 56-61.