|Table of Contents|

Car Whistle Recognition Based on Sub-Band SpectralEntropy Method and PSO-GA-SVM(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2021年02期
Page:
27-33
Research Field:
信息与通信工程
Publishing date:

Info

Title:
Car Whistle Recognition Based on Sub-Band SpectralEntropy Method and PSO-GA-SVM
Author(s):
Yu Linghao1; Lu Tiewen1; Li Chen1; Zeng Yumin1; Yuan Fang2
(1.School of Computer and Electronic Information/School of Artificial Intelligence,Nanjing Normal University,Nanjing 210023,China)(2.Hangzhou Aihua Intelligent Technology Co.,Ltd.,Hangzhou 311121,China)
Keywords:
car whistle recognition; sub-band spectral entropy method; support vector machine; particle swarm optimization; genetic algorithm
PACS:
TN912.34
DOI:
10.3969/j.issn.1672-1292.2021.02.005
Abstract:
In order to solve the problem of misjudgment in car whistle capture system,an algorithm based on sub-band spectral entropy and support vector machine is proposed in this paper. Firstly,sub-band spectral entropy method is used to preliminarily judge the sound samples. The samples whose sub-band spectral entropies are higher than a threshold value are directly determined as non-whistle samples. Then,the suspected whistle parts are segmented in the samples which are initially judged as whistle,and Mel frequency cepstrum coefficient is extracted as sound feature. Finally,the segmentation results are classified by support vector machine,and the parameters of support vector machine are optimized by the combination of particle swarm optimization and genetic algorithm. Simulation results show that the algorithm has good robustness. In the whistle recognition of actual collected samples,the algorithm also achieves a higher accuracy.

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Last Update: 2021-06-30