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Multimedia Tools and Applications

, Volume 75, Issue 23, pp 15509–15524 | Cite as

Comparative study of singing voice detection methods

  • Shingchern D. You
  • Yi-Chung Wu
  • Shih-Hsien Peng
Article

Abstract

Detecting Singing segments in a segment of a soundtrack is an important and useful technique in musical signal processing and retrieval. In this paper, we study the accuracy of detecting singing segments using the HMM (Hidden Markov Model) classifier with various features, including MFCC (Mel Frequency Cepstral Coefficients), LPCC (Linear Predictive Cepstral Coefficients), and LPC (Linear Prediction Coefficients). Simulation results show that detecting singing segments in a soundtrack is more difficult than detecting them among pure-instrument segments. In addition, combining MFCC and LPCC yield higher accuracy. The bootstrapping technique has only limited accuracy improvement to detect all singing segments in a soundtrack. To be complete, we also conduct an experiment to show that the time to perform music identification can be reduced by more than 40 % if we incorporate the singing-voice detection mechanism into the identification process.

Keywords

MFCC LPCC HMM vocal bootstrapping music identification 

Notes

Acknowledgments

This work was supported in part by National Science Council (NSC) and Ministry of Science and Technology (MOST) of Taiwan through Grants NSC 101-2221-E-027-127 and MOST 103-2221-E-027-092.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Shingchern D. You
    • 1
  • Yi-Chung Wu
    • 1
  • Shih-Hsien Peng
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Taipei University of TechnologyTaipeiTaiwan

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