Skip to main content
Book cover

International Conference on Independent Component Analysis and Signal Separation

ICA 2004: Independent Component Analysis and Blind Signal Separation pp 494–499Cite as

Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sources from Monophonic Inputs

Part of the Lecture Notes in Computer Science book series (LNCS,volume 3195)

Abstract

In this paper we present an extension to the Non-Negative Matrix Factorization algorithm which is capable of identifying components with temporal structure. We demonstrate the use of this algorithm in the magnitude spectrum domain, where we employ it to perform extraction of multiple sound objects from a single channel auditory scene.

Keywords

  • Nonnegative Matrix Factorization
  • Nonnegative Matrix
  • Positive Matrix Factorization
  • Input Sound
  • Spectral Basis

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-30110-3_63
  • Chapter length: 6 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   74.99
Price excludes VAT (USA)
  • ISBN: 978-3-540-30110-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Casey, M.A., Westner, A.: Separation of Mixed Audio Sources by Independent Subspace Analysis. In: Proceedings of the International Computer Music Conference, Berlin, Germany (August 2000)

    Google Scholar 

  • Lee, D.D., Seung, H.S.: Learning the parts of objects with nonnegative matrix factorization. Nature 401, 788–791 (1999)

    CrossRef  Google Scholar 

  • Lee, D.D., Seung, H.S.: Algorithms for Non-Negative Matrix Factorization. In: Neural Information Processing Systems 2000, pp. 556–562 (2000)

    Google Scholar 

  • Paatero, P.: Least Squares Formulation of Robust Non-Negative Factor Analysis. Chemometrics and Intelligent Laboratory Systems 37, 23–35 (1997)

    CrossRef  Google Scholar 

  • Smaragdis, P.: Redundancy Reduction for Computational Audition, a Unifying Approach. Doctoral Dissertation, MAS Dept. Massachusetts Institute of Technology, Cambridge MA, USA (2001)

    Google Scholar 

  • 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, New Paltz, NY (October 2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mitsubishi Electric Research Laboratories, 201 Broadway, Cambridge, MA, 02139, USA

    Paris Smaragdis

Authors
  1. Paris Smaragdis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet & Alberto Prieto & 

Rights and permissions

Reprints and Permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Smaragdis, P. (2004). Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sources from Monophonic Inputs. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30110-3_63

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive