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International Conference on Independent Component Analysis and Signal Separation

ICA 2006: Independent Component Analysis and Blind Signal Separation pp 700–707Cite as

Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 3889)

Abstract

We present a novel method for blind separation of instruments in single channel polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. The method is an extention of NMFD recently introduced by Smaragdis [1]. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to individual instruments. Based on this factorization we separate the instruments using spectrogram masking. The proposed algorithm has applications in computational auditory scene analysis, music information retrieval, and automatic music transcription.

Keywords

  • Nonnegative Matrix
  • Positive Matrix Factorization
  • Pitch Change
  • Music Information Retrieval
  • Music Signal

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/11679363_87
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References

  1. Smaragdis, P.: Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 494–499. Springer, Heidelberg (2004)

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Author information

Authors and Affiliations

  1. Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800, Kgs. Lyngby, Denmark

    Mikkel N. Schmidt & Morten Mørup

Authors
  1. Mikkel N. Schmidt
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  2. Morten Mørup
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Editor information

Editors and Affiliations

  1. Siemens Corporate Research, 755 College Road East, 08540, Princeton, NJ, USA

    Justinian Rosca

  2. Department of CSEE, Oregon Health and Science University, Portland, Oregon, USA

    Deniz Erdogmus

  3. Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA

    José C. Príncipe

  4. McMaster University, 1280 Main Street West, L8S 4K1, Hamilton, Ontario, Canada

    Simon Haykin

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© 2006 Springer-Verlag Berlin Heidelberg

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Schmidt, M.N., Mørup, M. (2006). Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_87

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  • DOI: https://doi.org/10.1007/11679363_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32630-4

  • Online ISBN: 978-3-540-32631-1

  • eBook Packages: Computer ScienceComputer Science (R0)