Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Vocal Separation from Mixtured Music

  • Kim, Hyun-Tae (Department of Multimedia Engineering, Dongeui University) ;
  • Park, Jang-Sik (Department of Electronics Engineering, Kyungsung University)
  • Received : 2011.02.27
  • Accepted : 2011.04.08
  • Published : 2011.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, According to increasing interest to original sound Karaoke instrument, MIDI type karaoke manufacturer attempt to make more cheap method instead of original recoding method. Separating technique for singing voice from music accompaniment is very useful in such equipment. We propose a system to separate singing voice from music accompaniment for stereo recordings. Our system consists of three stages. The first stage is a spectral change detector. The second stage classifies an input into vocal and non vocal portions by using GMM classifier. The last stage is a selective frequency separation stage. The results of removed by listening test from the results for computer based extraction simulation, spectrogram results show separation task successfully. Listening test with extracted MR from proposed system show vocal separating and removal task successfully.

Keywords

References

  1. J. Sundberg, "The acoustics of the singing voice," Scientific American, pp. 82-91, Mar. 1977.
  2. Y. E. Kim, "Singing voice analysis/synthesis," Ph.D. dissertation, MIT, Media Lab, 2003.
  3. D. L. Wang, "Feature-based speech segregation," in Computational Auditory Scene Analysis: Principles, Algorithms, and Applications, D. L. Wang and G. J. Brown, Eds. New York: IEEE Press (dual imprint with Wiley), 2006, to appear.
  4. C. Duxbury, J. P. Bello, M. Davies, and M. Sandler, "Complex domain onset detection for musical signals," in Proc. 6th Conf. Digital Audio Effect (DAFx-03), London, U.K., 2003.
  5. A. L. Berenzweig and D. P. W. Ellis, "Locating singing voice segments within music signals," in Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust., 2001, pp. 119-122.
  6. A. L. Berenzweig, D. P. W. Ellis, and S. Lawrence, "Using voice segments to improve artist classification of music," in Proceedings of AES 22nd International Conference on Virtual, Synthetic and Entertainment Audio, 2002.
  7. N. C. Maddage, C. Xu, and Y. Wang, "A SVM-based classification approach to musical audio," in Proc. ISMIR, 2003.
  8. D. Li, I. K. Sethi, N. Dimitrova, and T. McGee, "Classification of general audio data for content-based retrieval," Pattern Recognition Letters, vol. 22, pp. 533-544, 2002.
  9. D. Li, I. K. Sethi, N. Dimitrova, and T. McGee, "Classification of general audio data for content-based retrieval," Pattern Recognition Lett., vol. 22, pp. 533-544, 2002.
  10. M. Slaney, "Auditory Toolbox for MATLAB," Jan. 1999 [Online]. Available: http://rvl4.ecn.purdue.edu/-malcolm/interval/1998-010/
  11. K. Murphy, "HMM Toolbox for MATLAB," Jun. 2005 [Online]. Available: http://www.cs.ubc.ca/-murphyk/Software/HMM/hmm.html