Determined Blind Source Separation via Proximal Splitting Algorithm

Kohei Yatabe, Daichi Kitamura

    研究成果: Conference contribution

    13 被引用数 (Scopus)

    抄録

    The state-of-the-art algorithms of determined blind source separation (BSS) methods based on the independent component analysis (ICA) have gained computational efficiency by the majorization-minimization (MM) principle with a price of losing flexibility. That is, replacing and comparing different source models are not easy in such MM-based framework because it requires efforts to derive a new algorithm each time when one changes the model. In this paper, a general framework for obtaining an ICA-based BSS algorithm is proposed so that a source model can easily be replaced because only a single line of the algorithm must be modified. A sparsity-based extension of the independent vector analysis and a low-rankness-based BSS model using the nuclear norm are also proposed to demonstrate the simplicity and easiness of the proposed framework.

    本文言語English
    ホスト出版物のタイトル2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ776-780
    ページ数5
    2018-April
    ISBN(印刷版)9781538646588
    DOI
    出版ステータスPublished - 2018 9 10
    イベント2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
    継続期間: 2018 4 152018 4 20

    Other

    Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
    CountryCanada
    CityCalgary
    Period18/4/1518/4/20

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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