A probabilistic approach to AMDF pitch detection

@inproceedings{Ying1996APA,
  title={A probabilistic approach to AMDF pitch detection},
  author={Goangshiuan S. Ying and Leah H. Jamieson and Carl D. Mitchell},
  booktitle={ICSLP},
  year={1996}
}
We present a probabilistic error correction technique to be used with an average magnitude di erence function (AMDF) based pitch detector. This error correction routine provides a very simple method to correct errors in pitch period estimation. Used in conjunction with the computationally e cient AMDF, the result is a fast and accurate pitch detector. In performance tests on the CSTR (Center for Speech Technology Research) database, probabilistic error correction reduced the gross error rate… CONTINUE READING
Highly Cited
This paper has 17 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, and topics from this paper.

Key Quantitative Results

  • In performance tests on the CSTR (Center for Speech Technology Research) database, probabilistic error correction reduced the gross error rate from 6.07% to 3.29%.
14 Citations
13 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 13 references

Automatic prosody analysis

  • P. C. Bagshaw
  • PhD thesis,
  • 1994
Highly Influential
5 Excerpts

A feature-based time domain pitch tractor

  • M. S. Phillips
  • JASA, 77-S9-S10(A),
  • 1985
2 Excerpts

Pitch determination of speech signals: Algorithms and Devices

  • W. H. Hess
  • 1983
3 Excerpts

Realtime digital hardware pitch detector

  • J. J. Dubnowski, H. L. Sha er, L. R. Rabiner
  • IEEE Trans. ASSP,
  • 1976

Average magnitude di erence function pitch extractor

  • M. J. Ross, H. L. Sha er, A. Cohen, R. Freudberg, H. J. Manley
  • IEEE Trans. ASSP,
  • 1974
1 Excerpt

Similar Papers

Loading similar papers…