现代电子技术2017，Vol.40Issue(5)：155-158,4.DOI:10.16652/j.issn.1004-373x.2017.05.039
改进极限学习机的电子音乐分类模型
Electronic music classification model based on improved extreme learning machine
赵亮1
作者信息
- 1. 郑州大学 体育学院,河南 郑州 450000
- 折叠
摘要
Abstract
It is difficult to class and recognize the electronic music with the traditional model accurately,a new electronic music classification model based on improved extreme learning machine is proposed. The electronic music data is collected to ex-tract the feature of the cepstrum coefficient. The kernel principal component analysis is used to screen the feature. The genetic al-gorithm is used to select the parameters of the extreme learning machine to construct the classifier of the electronic music. The polytype electronic music is adopted to carry out the simulation experiments. The average classification rate of the electronic mu-sic can reach up to 95% with the improved extreme learning machine,and the wrong classification rate of the electronic music is far lower than that of other electronic music classification models. The feasibility and superiority of the electronic music classi-fication model were verified with the experimental results.关键词
音乐分类/核主成分分析/极限学习机/音乐特征/遗传算法Key words
music classification/kernel principal component analysis/extreme learning machine/music characteristic/ge-netic algorithm分类
信息技术与安全科学引用本文复制引用
赵亮..改进极限学习机的电子音乐分类模型[J].现代电子技术,2017,40(5):155-158,4.
