A factorial sparse coder model for single channel source separation

Robert Peharz, Michael Stark, Franz Pernkopf, Yannis Stylianou

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)

Abstract

We propose a probabilistic factorial sparse coder model for single channel source separation in the magnitude spectrogram domain. The mixture spectrogram is assumed to be the sum of the sources, which are assumed to be generated frame-wise as the output of sparse coders plus noise. For dictionary training we use an algorithm which can be described as non-negative matrix factorization with ℓ 0 sparseness constraints. In order to infer likely source spectrogram candidates, we approximate the intractable exact inference by maximizing the posterior over a plausible subset of solutions. We compare our system to the factorial-max vector quantization model, where the proposed method shows a superior performance in terms of signal-to-interference ratio. Finally, the low computational requirements of the algorithm allows close to real time applications.

Original languageEnglish
Title of host publicationAnnual Conference of the International Speech Communication Association (INTERSPEECH)
PublisherISCA
Pages386-389
Number of pages4
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan
Duration: 26 Sep 201030 Sep 2010

Conference

Conference11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010
Country/TerritoryJapan
CityMakuhari, Chiba
Period26/09/1030/09/10

Keywords

  • Source separation
  • Sparse coding
  • Sparse NMF

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