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 |
|---|---|
| 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 |
|---|---|
| Country | Canada |
| City | Calgary |
| Period | 18/4/15 → 18/4/20 |
Fingerprint
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
Cite this
Determined Blind Source Separation via Proximal Splitting Algorithm. / Yatabe, Kohei; Kitamura, Daichi.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. p. 776-780 8462338.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Determined Blind Source Separation via Proximal Splitting Algorithm
AU - Yatabe, Kohei
AU - Kitamura, Daichi
PY - 2018/9/10
Y1 - 2018/9/10
N2 - 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.
AB - 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.
KW - Frequency domain independent component analysis (FDICA)
KW - Independence-based separation
KW - Independent vector analysis (IVA)
KW - Primal-dual splitting algorithm
KW - Proximity operator
UR - http://www.scopus.com/inward/record.url?scp=85054277401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054277401&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8462338
DO - 10.1109/ICASSP.2018.8462338
M3 - Conference contribution
SN - 9781538646588
VL - 2018-April
SP - 776
EP - 780
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -