Online PLCA for Real-Time Semi-supervised Source Separation

  • Zhiyao Duan
  • Gautham J. Mysore
  • Paris Smaragdis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)


Non-negative spectrogram factorization algorithms such as probabilistic latent component analysis (PLCA) have been shown to be quite powerful for source separation. When training data for all of the sources are available, it is trivial to learn their dictionaries beforehand and perform supervised source separation in an online fashion. However, in many real-world scenarios (e.g. speech denoising), training data for one of the sources can be hard to obtain beforehand (e.g. speech). In these cases, we need to perform semi-supervised source separation and learn a dictionary for that source during the separation process. Existing semi-supervised separation approaches are generally offline, i.e. they need to access the entire mixture when updating the dictionary. In this paper, we propose an online approach to adaptively learn this dictionary and separate the mixture over time. This enables us to perform online semi-supervised separation for real-time applications. We demonstrate this approach on real-time speech denoising.


Current Frame Source Separation Nonnegative Matrix Factorization Activation Weight Noise Type 
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

  • Zhiyao Duan
    • 1
  • Gautham J. Mysore
    • 2
  • Paris Smaragdis
    • 2
    • 3
  1. 1.EECS DepartmentNorthwestern UniversityUSA
  2. 2.Advanced Technology LabsAdobe Systems IncUSA
  3. 3.University of Illinois at Urbana-ChampaignUSA

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