Complex NMF with the generalized Kullback-Leibler divergence

Hirokazu Kameoka, Hideaki Kagami, Masahiro Yukawa

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ56-60
ページ数5
ISBN(電子版)9781509041176
DOI
出版ステータスPublished - 2017 6月 16
イベント2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
継続期間: 2017 3月 52017 3月 9

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Other

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

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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