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Graph Embedded Multiple Kernel Extreme Learning Machine for Music Emotion Classification

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Advances in Brain Inspired Cognitive Systems (BICS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11691))

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Abstract

Music emotion classification is one of the most importance parts of music information retrieval (MIR) because of its potential commercial value and cultural value. However, music emotion classification is still a tough challenge, due to the low representation of music features. In this paper, a novel Extreme Learning Machine (ELM), combining graph regularization term and multiple kernel, is proposed to enhance the accuracy of music emotion classification. We use nonnegative matrix factorization (NMF) to find the optimal weights of combining multiple kernels. Furthermore, the graph regularization term is added to increase the relevance between predictions from the same class. The proposed Graph embedded Multiple Kernel Extreme Learning Machine (GMK-ELM) is tested on three music emotion datasets. Experiment results show that the proposed GMK-ELM outperforms several well-known ELM methods.

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Acknowledgement

This work is supported in part by the National Nature Science Foundation of China (nos. U1701266, 61471132), the Innovation Team Project of Guangdong Education Department (no. 2017KCXTD011), Natural Science Foundation of Guangdong Province China (no. 2018A030313751), and Science and Technology Program of Guangzhou, China (nos. 201803010065, 201802020010).

Author information

Authors and Affiliations

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Xixian Zhang, Zhijing Yang, Meilin Wang & Wing-Kuen Ling

  2. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, UK

    Jinchang Ren

Authors
  1. Xixian Zhang
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  2. Zhijing Yang
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  3. Jinchang Ren
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  4. Meilin Wang
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  5. Wing-Kuen Ling
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Corresponding author

Correspondence to Zhijing Yang .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  4. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Kaizhu Huang

  5. Northwestern Polytechnical University, Xi'an, China

    Jiangbin Zheng

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  7. Guangdong Polytechnic Normal University, Guangzhou, China

    Rongjun Chen

  8. Guangdong Polytechnic Normal University, Guangzhou, China

    Yinyin Xiao

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Zhang, X., Yang, Z., Ren, J., Wang, M., Ling, WK. (2020). Graph Embedded Multiple Kernel Extreme Learning Machine for Music Emotion Classification. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-39431-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39430-1

  • Online ISBN: 978-3-030-39431-8

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