TY - GEN
T1 - Determined Blind Source Separation via Proximal Splitting Algorithm
AU - Yatabe, Kohei
AU - Kitamura, Daichi
N1 - Funding Information:
This work was partly supported by JSPS Grant-in-Aid for Research Activity Start-up (17H06572,17H07191).
Funding Information:
∗This work was partly supported by JSPS Grant-in-Aid for Research Activity Start-up (17H06572,17H07191).
Publisher Copyright:
© 2018 IEEE.
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
AN - SCOPUS:85054277401
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
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.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -