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.
Original language | English |
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Title of host publication | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 776-780 |
Number of pages | 5 |
Volume | 2018-April |
ISBN (Print) | 9781538646588 |
DOIs | |
Publication status | Published - 2018 Sep 10 |
Event | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 2018 Apr 15 → 2018 Apr 20 |
Other
Other | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 |
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Country | Canada |
City | Calgary |
Period | 18/4/15 → 18/4/20 |
Keywords
- Frequency domain independent component analysis (FDICA)
- Independence-based separation
- Independent vector analysis (IVA)
- Primal-dual splitting algorithm
- Proximity operator
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering