Complex NMF with the generalized Kullback-Leibler divergence

Hirokazu Kameoka, Hideaki Kagami, Masahiro Yukawa

    研究成果: Conference contribution

    6 引用 (Scopus)

    抜粋

    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.

    元の言語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
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ56-60
    ページ数5
    ISBN(電子版)9781509041176
    DOI
    出版物ステータスPublished - 2017 6 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 52017 3 9

    Other

    Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
    United States
    New Orleans
    期間17/3/517/3/9

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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  • これを引用

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