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Determined Blind Source Separation via Proximal Splitting Algorithm
![](https://sigport.org/sites/default/files/styles/home/public/2018.04.20_ICASSP%E3%83%9D%E3%82%B9%E3%82%BF%E3%83%BC_PDS_ICA_0.png?itok=t43AjfHf)
- Citation Author(s):
- Submitted by:
- Kohei Yatabe
- Last updated:
- 20 April 2018 - 4:23am
- Document Type:
- Poster
- Document Year:
- 2018
- Event:
- Presenters:
- Kohei Yatabe
- Paper Code:
- AASP-P12.10
- Categories:
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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 majorizationminimization (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.