Ｒecognition of Instruments’Sounds Based on VMD and PSO
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 au...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2018-04-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1498 |
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| _version_ | 1849227090202722304 |
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| author | HUANG Ying-lai ＲEN Tian-li ZHAO Peng |
| author_facet | HUANG Ying-lai ＲEN Tian-li ZHAO Peng |
| author_sort | HUANG Ying-lai |
| collection | DOAJ |
| description | 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． |
| format | Article |
| id | doaj-art-82ff86631ee44bfa9a7c419a0a58f8d0 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2018-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-82ff86631ee44bfa9a7c419a0a58f8d02025-08-24T01:16:54ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832018-04-01230261110.15938/j.jhust.2018.02.002Ｒecognition of Instruments’Sounds Based on VMD and PSOHUANG Ying-lai0ＲEN Tian-li1ZHAO Peng2Information and Computer Engineering Collager，Northeast Forestry University，Harbin 150040，ChinaInformation and Computer Engineering Collager，Northeast Forestry University，Harbin 150040，ChinaInformation and Computer Engineering Collager，Northeast Forestry University，Harbin 150040，ChinaProposing 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．https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1498variational mode decompositionwavelet denoisingmel frequency cepstral coefficientsparticle swarm optimizationsupport vector machine |
| spellingShingle | HUANG Ying-lai ＲEN Tian-li ZHAO Peng Ｒecognition of Instruments’Sounds Based on VMD and PSO Journal of Harbin University of Science and Technology variational mode decomposition wavelet denoising mel frequency cepstral coefficients particle swarm optimization support vector machine |
| title | Ｒecognition of Instruments’Sounds Based on VMD and PSO |
| title_full | Ｒecognition of Instruments’Sounds Based on VMD and PSO |
| title_fullStr | Ｒecognition of Instruments’Sounds Based on VMD and PSO |
| title_full_unstemmed | Ｒecognition of Instruments’Sounds Based on VMD and PSO |
| title_short | Ｒecognition of Instruments’Sounds Based on VMD and PSO |
| title_sort | recognition of instruments sounds based on vmd and pso |
| topic | variational mode decomposition wavelet denoising mel frequency cepstral coefficients particle swarm optimization support vector machine |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1498 |
| work_keys_str_mv | AT huangyinglai recognitionofinstrumentssoundsbasedonvmdandpso AT rentianli recognitionofinstrumentssoundsbasedonvmdandpso AT zhaopeng recognitionofinstrumentssoundsbasedonvmdandpso |