Determined Blind Source Separation via Proximal Splitting Algorithm

Kohei Yatabe; Daichi Kitamura

ICASSP 2018accepted0 citations

Determined Blind Source Separation via Proximal Splitting Algorithm

Abstract

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.

BibTeX

@inproceedings{icassp2018_determinedblinds,
  title = {Determined Blind Source Separation via Proximal Splitting Algorithm},
  author = {Kohei Yatabe and Daichi Kitamura},
  booktitle = {ICASSP 2018},
  year = {2018}
}