Sound Recognition in Mixtures

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


In this paper, we describe a method for recognizing sound sources in a mixture. While many audio-based content analysis methods focus on detecting or classifying target sounds in a discriminative manner, we approach this as a regression problem, in which we estimate the relative proportions of sound sources in the given mixture. Using source separation ideas based on probabilistic latent component analysis, we directly estimate these proportions from the mixture without actually separating the sources. We also introduce a method for learning a transition matrix to temporally constrain the problem. We demonstrate the proposed method on a mixture of five classes of sounds and show that it is quite effective in correctly estimating the relative proportions of the sounds in the mixture.


Relative Proportion Transition Matrix Sound Source Single Source Transition Matrice 
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

  • Juhan Nam
    • 1
  • Gautham J. Mysore
    • 2
  • Paris Smaragdis
    • 2
    • 3
  1. 1.Center for Computer Research in Music and AcousticsStanford UniversityUSA
  2. 2.Advanced Technology LabsAdobe Systems Inc.USA
  3. 3.University of Illinois at Urbana-ChampaignUSA

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