Algorithm Research & Explore
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2031-2034

Research on musical instrument classification based on deep Boltzmann machine

Zhou Chang
Mi Hongjuan
College of Information Engineering, Lanzhou University of Finance & Economics, Lanzhou 730020, China

Abstract

The application of traditional shallow model to instrument classification task has the problem of poor nonlinear fitting ability, so that the accuracy of classification was not guaranteed effectively. It is necessary to introduce deep learning method to improve the nonlinear modeling ability of complex tasks. This paper used deep Boltzmann machine as feature extractor to abstract more expressive deep learning features. It respectively used SVM and softmax classifier as top layer of deep neural network to form DBM+SVM and DBM+softmax combined model. Besides, this paper introduced the mean field theory and momentum factor to optimize the network training process, and compared the above two sets of models and single SVM classifier on 5 kinds of musical instruments audio data. The classification accuracy of the two types of deep learning combination models reached 89.29% and 87.5% respectively, compared with the accuracy of the traditional shallow classification method SVM of 73.21%. The experimental results show that the application of deep Boltzmann machine in the field of musical instrument classification is very promising.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.01.0022
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 7
Section: Algorithm Research & Explore
Pages: 2031-2034
Serial Number: 1001-3695(2019)07-026-2031-04

Publish History

[2019-07-05] Printed Article

Cite This Article

周畅, 米红娟. 基于深度玻尔兹曼机的乐器分类问题研究 [J]. 计算机应用研究, 2019, 36 (7): 2031-2034. (Zhou Chang, Mi Hongjuan. Research on musical instrument classification based on deep Boltzmann machine [J]. Application Research of Computers, 2019, 36 (7): 2031-2034. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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