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Multimedia Tools and Applications

, Volume 78, Issue 1, pp 161–176 | Cite as

Music auto-tagging based on the unified latent semantic modeling

  • Xi ShaoEmail author
  • Zhiyong Cheng
  • Mohan S. Kankanhalli
Article
  • 110 Downloads

Abstract

We proposed a music auto-tagging approach based on the latent space modeling both for music context and content. First, we introduce the latent semantic analysis for music tags with Sparse Nonnegative Matrix Factorization. Then the music contents semantics will be learnt by decomposing the music content into a pre-trained dictionary and an adaptive dictionary learning algorithm is proposed. Finally, the two latent spaces will be associated with a certain subspace mapping algorithm. The experimental results show that our proposed approach outperforms the state-of-the-art auto-tagging systems when applied to the CAL500 dataset in the 5-fold cross-validation experiments.

Keywords

Music tag Latent semantic analysis Music recommendation 

Notes

Acknowledgments

This work is supported by the National Nature Science Foundation of China under Grant No. 60902065, No. 61401227, and by Beijing Natural Science Foundation (No.4152053).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xi Shao
    • 1
    • 2
    Email author
  • Zhiyong Cheng
    • 2
  • Mohan S. Kankanhalli
    • 2
  1. 1.College of Communication and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.School of ComputingNational University of SingaporeSingaporeSingapore

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