A New Adaptive Filter Algorithm for System Identification Using Independent Component Analysis(<Special Section>Papers Selected from the 21st Symposium on Signal Processing)
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概要
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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.
- 社団法人電子情報通信学会の論文
- 2007-08-01
著者
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SAKAI Hideaki
Graduate School of Engineering, Gunma University
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Yang Jun-mei
Graduate School Of Informatics Kyoto University
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Sakai Hideaki
Graduate School Of Engineering Gunma University
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YANG Jun-Mei
Graduate School of Informatics, Kyoto University
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