Complex NMF with the generalized Kullback-Leibler divergence

Hirokazu Kameoka, Hideaki Kagami, Masahiro Yukawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

We previously introduced a phase-aware variant of the non-negative matrix factorization (NMF) approach for audio source separation, which we call the 'Complex NMF (CNMF).' This approach makes it possible to realize NMF-like signal decompositions in the complex time-frequency domain. One limitation of the CNMF framework is that the divergence measure is limited to only the Euclidean distance. Some previous studies have revealed that for source separation tasks with NMF, the generalized Kullback-Leibler (KL) divergence tends to yield higher accuracy than when using other divergence measures. This motivated us to believe that CNMF could achieve even greater source separation accuracy if we could derive an algorithm for a KL divergence counterpart of CNMF. In this paper, we start by defining the notion of the 'dual' form of the CNMF formulation, derived from the original Euclidean CNMF, and show that a KL divergence counterpart of CNMF can be developed based on this dual formulation. We call this 'KL-CNMF'. We further derive a convergence-guaranteed iterative algorithm for KL-CNMF based on a majorization-minimization scheme. The source separation experiments revealed that the proposed KL-CNMF yielded higher accuracy than the Euclidean CNMF and NMF with varying divergences.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages56-60
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 2017 Jun 16
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 2017 Mar 52017 Mar 9

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period17/3/517/3/9

Keywords

  • Audio source separation
  • Complex NMF
  • generalized Kullback-Leibler (KL) divergence
  • non-negative matrix factorization (NMF)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kameoka, H., Kagami, H., & Yukawa, M. (2017). Complex NMF with the generalized Kullback-Leibler divergence. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 56-60). [7952117] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952117