IEICE TRANSACTIONS on Fundamentals
A New Adaptive Filter Algorithm for System Identification Using Independent Component Analysis
Summary :
This paper proposes a new adaptive filter algorithm for system identification by using an independent component analysis (ICA) technique, which separates the signal from noisy observation under the assumption that the signal and noise are independent. We first introduce an augmented state-space expression of the observed signal, representing the problem in terms of ICA. By using a nonparametric Parzen window density estimator and the stochastic information gradient, we derive an adaptive algorithm to separate the noise from the signal. The proposed ICA-based algorithm does not suppress the noise in the least mean square sense but to maximize the independence between the signal part and the noise. The computational complexity of the proposed algorithm is compared with that of the standard NLMS algorithm. The stationary point of the proposed algorithm is analyzed by using an averaging method. We can directly use the new ICA-based algorithm in an acoustic echo canceller without double-talk detector. Some simulation results are carried out to show the superiority of our ICA method to the conventional NLMS algorithm.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.8 pp.1549-1554
- Publication Date
- 2007/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e90-a.8.1549
- Type of Manuscript
- Special Section PAPER (Special Section on Papers Selected from the 21st Symposium on Signal Processing)
- Category
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Jun-Mei YANG, Hideaki SAKAI, "A New Adaptive Filter Algorithm for System Identification Using Independent Component Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 8, pp. 1549-1554, August 2007, doi: 10.1093/ietfec/e90-a.8.1549.
Abstract: This paper proposes a new adaptive filter algorithm for system identification by using an independent component analysis (ICA) technique, which separates the signal from noisy observation under the assumption that the signal and noise are independent. We first introduce an augmented state-space expression of the observed signal, representing the problem in terms of ICA. By using a nonparametric Parzen window density estimator and the stochastic information gradient, we derive an adaptive algorithm to separate the noise from the signal. The proposed ICA-based algorithm does not suppress the noise in the least mean square sense but to maximize the independence between the signal part and the noise. The computational complexity of the proposed algorithm is compared with that of the standard NLMS algorithm. The stationary point of the proposed algorithm is analyzed by using an averaging method. We can directly use the new ICA-based algorithm in an acoustic echo canceller without double-talk detector. Some simulation results are carried out to show the superiority of our ICA method to the conventional NLMS algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.8.1549/_p
Copy
@ARTICLE{e90-a_8_1549,
author={Jun-Mei YANG, Hideaki SAKAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A New Adaptive Filter Algorithm for System Identification Using Independent Component Analysis},
year={2007},
volume={E90-A},
number={8},
pages={1549-1554},
abstract={This paper proposes a new adaptive filter algorithm for system identification by using an independent component analysis (ICA) technique, which separates the signal from noisy observation under the assumption that the signal and noise are independent. We first introduce an augmented state-space expression of the observed signal, representing the problem in terms of ICA. By using a nonparametric Parzen window density estimator and the stochastic information gradient, we derive an adaptive algorithm to separate the noise from the signal. The proposed ICA-based algorithm does not suppress the noise in the least mean square sense but to maximize the independence between the signal part and the noise. The computational complexity of the proposed algorithm is compared with that of the standard NLMS algorithm. The stationary point of the proposed algorithm is analyzed by using an averaging method. We can directly use the new ICA-based algorithm in an acoustic echo canceller without double-talk detector. Some simulation results are carried out to show the superiority of our ICA method to the conventional NLMS algorithm.},
keywords={},
doi={10.1093/ietfec/e90-a.8.1549},
ISSN={1745-1337},
month={August},}
Copy
TY - JOUR
TI - A New Adaptive Filter Algorithm for System Identification Using Independent Component Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1549
EP - 1554
AU - Jun-Mei YANG
AU - Hideaki SAKAI
PY - 2007
DO - 10.1093/ietfec/e90-a.8.1549
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2007
AB - This paper proposes a new adaptive filter algorithm for system identification by using an independent component analysis (ICA) technique, which separates the signal from noisy observation under the assumption that the signal and noise are independent. We first introduce an augmented state-space expression of the observed signal, representing the problem in terms of ICA. By using a nonparametric Parzen window density estimator and the stochastic information gradient, we derive an adaptive algorithm to separate the noise from the signal. The proposed ICA-based algorithm does not suppress the noise in the least mean square sense but to maximize the independence between the signal part and the noise. The computational complexity of the proposed algorithm is compared with that of the standard NLMS algorithm. The stationary point of the proposed algorithm is analyzed by using an averaging method. We can directly use the new ICA-based algorithm in an acoustic echo canceller without double-talk detector. Some simulation results are carried out to show the superiority of our ICA method to the conventional NLMS algorithm.
ER -
English
