Phase recovery in NMF for audio source separation: An insightful benchmark

@article{Magron2015PhaseRI,
  title={Phase recovery in NMF for audio source separation: An insightful benchmark},
  author={Paul Magron and Roland Badeau and Bertrand David},
  journal={2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2015},
  pages={81-85}
}
  • Paul Magron, Roland Badeau, Bertrand David
  • Published in
    IEEE International Conference…
    2015
  • Computer Science
  • Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of audio signals in the Time-Frequency (TF) domain. [...] Key Method For each model considered, a comparison between two approaches (blind separation without prior information and oracle separation with supervised model learning) is performed, in order to inquire about the room for improvement for the estimation methods. Experimental results show that the High Resolution NMF (HRNMF) model is particularly promising, because it is…Expand Abstract

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    Phase-aware Harmonic/percussive Source Separation via Convex Optimization

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    Bayesian Anisotropic Gaussian Model for Audio Source Separation

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    Model-Based Phase Recovery of Spectrograms via Optimization on Riemannian Manifolds

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 21 REFERENCES

    Performance measurement in blind audio source separation

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Algorithms for No nnegative Matrix Factorization

    • Daniel D. Lee, H. Sebastian Seung
    • inAdvances in Neural Information Processing Systems 13, pp. 556–562. MIT Press, 2001.
    • 2001
    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    Multichannel High-Resolution NMF for Modeling Convolutive Mixtures of Non-Stationary Signals in the Time-Frequency Domain

    VIEW 2 EXCERPTS

    , and Cédric Févotte , “ Performance Measurement in Blind Audio Source Separation

    • Rémi Gribonval Emmanuel. Vincent
    • IEEE Transactions on Acoustics , Speech and Signal Processing Proc . ISCA Workshop on Statistical and Perceptual Audition ( SAPA ) Proc . IEEE Workshop on Applications of Signal Processing to Audio and Acoustics ( WASPAA ) , New Paltz , NY , USA , October
    • 2011

    Gaussian modeling of mixtures of non-stationary signals in the Time-Frequency domain (HR-NMF)

    • Roland Badeau
    • Computer Science
    • 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
    • 2011
    VIEW 3 EXCERPTS

    Complex NMF: A new sparse representation for acoustic signals

    VIEW 2 EXCERPTS