Discriminative layered nonnegative matrix factorization for speech separation

Chung Chien Hsu, Tai-Shih Chi, Jen-Tzung Chien

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

This paper proposes a discriminative layered nonnegative matrix factorization (DL-NMF) for monaural speech separation. The standard NMF conducts the parts-based representation using a single-layer of bases which was recently upgraded to the layered NMF (L-NMF) where a tree of bases was estimated for multi-level or multi-aspect decomposition of a complex mixed signal. In this study, we develop the DL-NMF by extending the generative bases in L-NMF to the discriminative bases which are estimated according to a discriminative criterion. The discriminative criterion is conducted by optimizing the recovery of the mixed spectra from the separated spectra and minimizing the reconstruction errors between separated spectra and original source spectra. The experiments on single-channel speech separation show the superiority of DL-NMF to NMF and L-NMF in terms of the SDR, SIR and SAR measures.

Original languageEnglish
Pages (from-to)560-564
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume08-12-September-2016
DOIs
StatePublished - 1 Jan 2016
Event17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
Duration: 8 Sep 201616 Sep 2016

Keywords

  • Dictionary learning
  • Discriminative learning
  • Nonnegative matrix factorization
  • Speech separation

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