Itakura-Saito Nonnegative Factorizations of the Power Spectrogram for Music Signal Decomposition

@inproceedings{Fvotte2010ItakuraSaitoNF,
  title={Itakura-Saito Nonnegative Factorizations of the Power Spectrogram for Music Signal Decomposition},
  author={C{\'e}dric F{\'e}votte and T{\'e}l{\'e}com ParisTech},
  year={2010}
}
Nonnegative matrix factorization (NMF) is a popular linear regression technique in the fields of machine learning and signal/image processing. Much research about this topic has been driven by applications in audio. NMF has been for example applied with success to automatic music transcription and audio source separation, where the data is usually taken as the magnitude spectrogram of the sound signal, and the Euclidean distance or Kullback-Leibler divergence are used as measures of fit between… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 49 references

Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation

IEEE Transactions on Audio, Speech, and Language Processing • 2010
View 3 Excerpts

Factorial Scaled Hidden Markov Model for polyphonic audio representation and source separation

2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics • 2009
View 2 Excerpts

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