The 2011 Signal Separation Evaluation Campaign (SiSEC2011): - Audio Source Separation -

  • Shoko Araki
  • Francesco Nesta
  • Emmanuel Vincent
  • Zbyněk Koldovský
  • Guido Nolte
  • Andreas Ziehe
  • Alexis Benichoux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)


This paper summarizes the audio part of the 2011 community-based Signal Separation Evaluation Campaign (SiSEC2011). Four speech and music datasets were contributed, including datasets recorded in noisy or dynamic environments and a subset of the SiSEC2010 datasets. The participants addressed one or more tasks out of four source separation tasks, and the results for each task were evaluated using different objective performance criteria. We provide an overview of the audio datasets, tasks and criteria. We also report the results achieved with the submitted systems, and discuss organization strategies for future campaigns.


Source Separation Blind Source Separation Nonnegative Matrix Factorization Speech Enhancement Reverberation Time 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shoko Araki
    • 1
  • Francesco Nesta
    • 2
  • Emmanuel Vincent
    • 3
  • Zbyněk Koldovský
    • 4
  • Guido Nolte
    • 5
  • Andreas Ziehe
    • 5
  • Alexis Benichoux
    • 3
  1. 1.NTT Communication Science Labs.NTT CorporationJapan
  2. 2.Center of Information TechnologyFondazione Bruno Kessler - IrstItaly
  3. 3.Centre Inria RennesINRIABretagne AtlantiqueFrance
  4. 4.Technical University of LiberecCzech Republic
  5. 5.Fraunhofer Institute FIRST IDAGermany

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