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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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  2. Paatero, P., Tapper, U.: Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111–126 (1994)

    CrossRef  Google Scholar 

  3. Lee, D., Seung, H.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)

    CrossRef  Google Scholar 

  4. Donoho, D., Stodden, V.: When does non-negative matrix factorization give a correct decomposition into parts? In: NIPS (2003)

    Google Scholar 

  5. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556–562 (2000)

    Google Scholar 

  6. Helén, M., Virtanen, T.: Separation of drums from polyphonic music using nonnegative matrix factorization and support vector machine. In: 13th European Signal Processing Conference (2005)

    Google Scholar 

  7. Smaragdis, P., Brown, J.C.: Non-negative matrix factorization for polyphonic music transcription. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 177–180 (2003)

    Google Scholar 

  8. Wang, B., Plumbley, M.D.: Musical audio stream separation by non-negative matrix factorization. In: Proceedings of the DMRN Summer Conference (2005)

    Google Scholar 

  9. Virtanen, T.: Separation of sound sources by convolutive sparse coding. SAPA (2004)

    Google Scholar 

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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)