[1]黄英来,任田丽,赵鹏. VMD 与PSO 的乐器声音识别[J].哈尔滨理工大学学报,2018,(02):6-11.[doi:10. 15938 /j. jhust. 2018. 02. 002]
 HUANG Ying-lai,EN Tian-li,ZHAO Peng. Recognition of Instruments’Sounds Based on VMD and PSO[J].哈尔滨理工大学学报,2018,(02):6-11.[doi:10. 15938 /j. jhust. 2018. 02. 002]
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 VMD 与PSO 的乐器声音识别()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2018年02期
页码:
6-11
栏目:
材料科学与工程
出版日期:
2018-04-25

文章信息/Info

Title:
 Recognition of Instruments’Sounds Based on VMD and PSO
文章编号:
1007- 2683( 2018) 02- 0006- 06
作者:
 黄英来 任田丽 赵鹏
 ( 东北林业大学信息与计算机工程学院,黑龙江哈尔滨150040)
Author(s):
 HUANG Ying-lai REN Tian-li ZHAO Peng
( Information and Computer Engineering Collager,Northeast Forestry University,Harbin 150040,China)
关键词:
 (变分模态分解 小波去噪 梅尔频率倒谱系数 粒子群算法 支持向量机
Keywords:
 variational mode decomposition wavelet denoising Mel frequency cepstral coefficients particleswarm optimization support vector machine
分类号:
TP391
DOI:
10. 15938 /j. jhust. 2018. 02. 002
文献标志码:
A
摘要:
 针对乐器音频信号的识别率低的问题,提出了一种变分模态分解( VMD) 和被粒子群
算法( PSO) 优化的支持向量机( SVM) 的乐器音频信号识别的方法。采用VMD 将乐器音频信号分
解成一系列平稳的窄带分量( IMF) ,并根据相关系数重构信号,采用小波去除残余的噪声。最后,
在分析传统的声音特征提取方法基础上,提取梅尔频率倒谱系数( MFCC) ,用经PSO 寻优参数的
SVM 进行音频信号的分类。实验结果表明,本文算法的去噪效果明显优于经验模态分解( EMD) 和
集合经验模态分解( EEMD) 的分析结果; PSO 优化后的SVM 有效的提高了噪声环境下音频信号分
类的正确率。
Abstract:
 Proposing the method that based on the variational mode decomposition ( VMD) and particle swarm
optimization ( PSO) optimized support vector machine ( SVM) are used to recognize the audio signals of the musical
instruments aiming at the problem of the low recognition rate of musical instruments audio signals. In this paper,
firstly,the instrument audio signals are decomposed into a series of stable narrowband components ( IMF) by
VMD. After decomposition,according to the correlation coefficient we reconstruct the signals,then using the
wavelet to remove the residual noises. Finally,based on the analysis of the traditional sound features extraction
method,extracting the Mel frequency cepstral coefficients ( MFCC) and then SVM whose parameters are optimized
by PSO is used to recognize the audio signals. This expserimental results show that the denoising effect of the
proposed algorithm in this paper is better than that of empirical mode decomposition ( EMD) and ensemble
empirical mode decomposition ( EEMD) ; SVM optimized by PSO effectively improve the accuracy of audio signals’
classification in noisy environment.

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更新日期/Last Update: 2018-06-09