Multiple-Feature Fusion Based Onset Detection for Solo Singing Voice

@inproceedings{Toh2008MultipleFeatureFB,
  title={Multiple-Feature Fusion Based Onset Detection for Solo Singing Voice},
  author={Chee-Chuan Toh and Bingjun Zhang and Ye Wang},
  booktitle={ISMIR},
  year={2008}
}
Onset detection is a challenging problem in automatic singing transcription. In this paper, we address singing onset detection with three main contributions. First, we outline the nature of a singing voice and present a new singing onset detection approach based on supervised machine learning. In this approach, two Gaussian Mixture Models (GMMs) are used to classify audio features of onset frames and non-onset frames. Second, existing audio features are thoroughly evaluated for this approach to… CONTINUE READING
Highly Cited
This paper has 27 citations. REVIEW CITATIONS
22 Citations
15 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 22 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 15 references

A comparison of sound onset detection algorithms with emphasis on psycho-acoustically motivated detection functions

  • N. Collins
  • Proceedings of AES118 Convention,
  • 2005
Highly Influential
6 Excerpts

Spectral Processing of the Singing Voice

  • A. Loscos
  • Ph.D. Thesis submission to Pompeu Fabra…
  • 2007
1 Excerpt

Predictive Coding and Cepstrum coefficients for mining time variant information from software repositories

  • G. Antoniol, V. Rollo, G. Venturi “Linear
  • International Workshop on Mining Software…
  • 2005
1 Excerpt

Similar Papers

Loading similar papers…