TY - JOUR
T1 - Dual multivariate auto-regressive modeling in state space for temporal signal separation
AU - Cheung, Yiu Ming
AU - Xu, Lei
N1 - Funding Information:
Manuscript received September 23, 2000; revised December 27, 2001. This work was supported by the Research Grant Council of the Hong Kong SAR under Project CUHK4169/00E and by a Faculty Research Grant of Hong Kong Baptist University under Project Code FRG/01-02/II-24. This paper was recommended by Associate Editor I. Gu.
PY - 2003/6
Y1 - 2003/6
N2 - Many existing independent component analysis (ICA) approaches result in deteriorated performance in temporal source separation because they have not taken into consideration of the underlying temporal structure of sources. In this paper, we model temporal sources as a general multivariate auto-regressive (AR) process whereby an underlying multivariate AR process in observation space is obtained. In this dual AR modeling, the mixing process from temporal sources to observations is the same as the mixture from the nontemporal residuals of the source AR (SAR) process to that of the observation AR (OAR) process. We can therefore avoid the source temporal effects in performing ICA by learning the demixing system on the independently distributed OAR residuals rather than the time-correlated observations. Particularly, we implement this approach by modeling each source signal as a finite mixture of generalized autoregressive conditional heteroskedastic (GARCH) process. The adaptive algorithms are proposed to extract the OAR residuals appropriately online, together with learning the demixing system via a nontemporal ICA algorithm. The experiments have shown its superior performance on temporal source separation.
AB - Many existing independent component analysis (ICA) approaches result in deteriorated performance in temporal source separation because they have not taken into consideration of the underlying temporal structure of sources. In this paper, we model temporal sources as a general multivariate auto-regressive (AR) process whereby an underlying multivariate AR process in observation space is obtained. In this dual AR modeling, the mixing process from temporal sources to observations is the same as the mixture from the nontemporal residuals of the source AR (SAR) process to that of the observation AR (OAR) process. We can therefore avoid the source temporal effects in performing ICA by learning the demixing system on the independently distributed OAR residuals rather than the time-correlated observations. Particularly, we implement this approach by modeling each source signal as a finite mixture of generalized autoregressive conditional heteroskedastic (GARCH) process. The adaptive algorithms are proposed to extract the OAR residuals appropriately online, together with learning the demixing system via a nontemporal ICA algorithm. The experiments have shown its superior performance on temporal source separation.
KW - Blind signal separation
KW - Dual auto-regressive processes
KW - Generalized autoregressive conditional heteroskedastic (GARCH) model
KW - Independent component analysis
UR - https://doi.org/10.1109/TSMCB.2003.815968
UR - http://www.scopus.com/inward/record.url?scp=0038660018&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2003.811132
DO - 10.1109/TSMCB.2003.811132
M3 - Journal article
AN - SCOPUS:0038660018
SN - 1083-4419
VL - 33
SP - 386
EP - 398
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 3
ER -