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Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation

  • Mikkel N. Schmidt
  • Morten Mørup
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mikkel N. Schmidt
    • 1
  • Morten Mørup
    • 1
  1. 1.Informatics and Mathematical ModellingTechnical University of DenmarkKgs. LyngbyDenmark

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