The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
권호 표지
DOI QR코드

DOI QR Code

A Robust Audio Fingerprinting Method Based on Segmentation Boundaries

  • Seo, Jin-Soo (Dept. of Electrical Engineering, Gangneung-Wonju National University)
  • Received : 2012.02.06
  • Accepted : 2012.03.06
  • Published : 2012.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

A robust audio fingerprinting method is presented based on segmentation boundaries. In order to obtain robustness against linear speed changes, fingerprint extraction and matching are synchronized with the segmentation boundaries. Experimental results show that the proposed method is also robust against other common audio processing steps including low bit-rate compression, equalization, and time-scale modification.

Keywords

References

  1. J. Haitsma and T. Kalker, "A highly robust audio fingerprinting system," Proc. ISMIR 2002, pp. 144-148, 2002.
  2. J. Seo, M. Jin, S. Lee, D. Jang, S. Lee, and C. Yoo, "Audio fingerprinting based on normalized spectral subband moments," IEEE Signal Processing Letters, vol. 13, no. 4, pp. 209-212, Apr. 2006. https://doi.org/10.1109/LSP.2005.863678
  3. J. Seo and S. Lee, "Robust audio fingerprinting using compressed-domain features," Journal of Acoustical Society of Korea, vol. 28, no. 4, pp. 375-382, May 2009.
  4. P. Cano, E. Batlle, T. Kalker, and J. Haitsma, "A review of algorithms for audio fingerprinting," Proc. IEEE Workshop on Multimedia Signal Processing, pp. 169-173, 2002.
  5. J. Haitsma and T. Kalker, "Speed-change resistant audio fingerprinting using auto-correlation," Proc. IEEE ICASSP 2003, pp. 728-731, 2003.
  6. J. Bello, C. Duxbury, M. Davies, and M. Sandler, "On the use of phase and energy for musical onset detection in the complex domain," IEEE Signal Processing Letters, vol. 11, no. 6, pp. 553-556, June 2004. https://doi.org/10.1109/LSP.2004.827951
  7. E. Zwicker and H. Fastl, Psychoacoustics: Facts and Models, Springer-Verlag, 1999.
  8. J. Seo, M. Jin, S. Lee, D. Jang, S. Lee, and C. Yoo, "Audio fingerprinting based on normalized spectral subband centroids," Proc. IEEE ICASSP 2005, pp. 213-216, 2005.
  9. M. Mohri, P. Moreno, and E. Weinstein, "Efficient and robust music identification with weighted finitestate transducers," IEEE Tr. Audio, Speech, and Language Processing, vol. 18, no. 1, pp. 197-207, Jan. 2010. https://doi.org/10.1109/TASL.2009.2023170
  10. N. Chen, H. Xiao, and W. Wan, "Audio hash function based on non-negative matrix factorisation of melfrequency cepstral coefficients," IET Information Security, vol. 5, no. 1, pp. 19-25, Mar. 2011. https://doi.org/10.1049/iet-ifs.2010.0097
  11. E. Dupraz and G. Richard, "Robust frequency-based audio fingerprinting," Proc. IEEE ICASSP 2010, pp. 281-284, 2010.
  12. B. Zhu, W. Li, Z. Wang, and X. Xue, "A novel audio fingerprinting method robust to time scale modification and pitch shifting," Proc. ACM Multimedia 2010, pp. 987-990, 2010.

Cited by

  1. Centroid-model based music similarity with alpha divergence vol.35, pp.2, 2016, https://doi.org/10.7776/ASK.2016.35.2.083
  2. A Study on the Performance of Music Retrieval Based on the Emotion Recognition vol.34, pp.3, 2015, https://doi.org/10.7776/ASK.2015.34.3.247