Graph embedded multiple kernel extreme learning machine for music emotion classification

Xixian Zhang, Zhijing Yang, Jinchang Ren, Meilin Wang, Wing-Kuen Ling

Research output: Chapter in Book/Report/Conference proceedingConference contribution book


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.
Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems
Subtitle of host publication10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings
EditorsJinchang Ren, Amir Hussain, Huimin Zhao, Kaizhu Huang, Jiangbin Zheng, Jun Cai, Rongjun Chen, Yinyin Xiao
Place of PublicationCham, Switzerland
Number of pages12
ISBN (Electronic)9783030394318
ISBN (Print)9783030394301
Publication statusPublished - 1 Feb 2020
Event10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, China
Duration: 13 Jul 201914 Jul 2019


Conference10th International Conference on Brain Inspired Cognitive Systems, BICS 2019


  • music emotion classification
  • extreme learning machine (ELM)
  • graph embedded
  • multiple kernel learning

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