Live Drum Separation Using Probabilistic Spectral Clustering Based on the Itakura-Saito Divergence

@inproceedings{Battenberg2011LiveDS,
  title={Live Drum Separation Using Probabilistic Spectral Clustering Based on the Itakura-Saito Divergence},
  author={Eric Battenberg and Victoria Huang and David Wessel},
  year={2011}
}
We present a live drum separation system for a specific target drumset to be used as a front end in a complete live drum understanding system. Our system decomposes drum note onsets onto spectral drum templates by adapting techniques from non-negative matrix factorization. Multiple templates per drum are computed using a new Gamma mixture model clustering procedure to account for the variety of sounds that can be produced by a single drum. This clustering procedure imposes an Itakura-Saito… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-9 of 9 extracted citations

A Review of Automatic Drum Transcription

IEEE/ACM Transactions on Audio, Speech, and Language Processing • 2018
View 3 Excerpts

An open-source drum transcription system for Pure Data and Max MSP

2013 IEEE International Conference on Acoustics, Speech and Signal Processing • 2013
View 3 Excerpts

References

Publications referenced by this paper.
Showing 1-10 of 20 references

Improvements to Percussive Component Extraction Using Non-Negative Matrix Factorization and Support Vector Machines

E. Battenberg
Master’s thesis, University of California, Berkeley, Dec. 2008. • 2008
View 1 Excerpt

Minimum description length

Scholarpedia • 2008
View 2 Excerpts

A tutorial on onset detection in music signals

J. Bello
IEEE Transactions on Speech and Audio Processing, vol. 13, no. 5, p. 1035, 2005. • 2005
View 1 Excerpt

Similar Papers

Loading similar papers…