Convolutive Independent Component Analysis Based on First-order Statistics for Complex-valued Source Extraction

Xu, Pengcheng; Shen, Yuehong

doi:10.14257/ijsip.2016.9.1.24

Cited by 2 publications

(2 citation statements)

References 17 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the first-order statistics-based algorithms [6][7][8] generally extract the source of interest under the condition that the mixing matrix is a unitary matrix, they can be only used in the determined case. In this paper, we remove this condition and obtain the column vector of the mixing matrix corresponding to the desired source based on the conditional expectation shown by…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…The existing extraction algorithms mainly employ the second-and higher-order statistics by exploiting the statistical independence of the sources, and some of them need to preprocess the mixture data such as FastICA [5]. Recently, Zarzoso et al [6] and Xu et al [7] and Xu and Shen [8] proposed a class of more computationally efficient algorithms for independent source extraction based on the first-order statistics as well as the conditional expectation. But before performing this class of algorithm, it requires to know the time indices where the source of interest is positive, that is, the positive support of the desired source, which reduces the practicability of this class of algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Extraction for Skewed Source Signals Using Conditional Expectation

Wei

Deng

et al. 2018

Journal of Sensors

View full text Add to dashboard Cite

The extraction of the stochastic source signals whose probability density functions (PDFs) are skewed is very important in many applications such as biomedical signal processing and mechanical fault diagnosis. This paper shows that the skewed source signal with the maximal absolute value of skewness can be fast extracted by a proposed algorithm using conditional expectation. Compared with the existing conditional expectation-based algorithms, the proposed one possesses two main advantages. One is that it does not require the prior knowledge of the positive support of the desired source, namely the time indices where the source of interest is positive. The other is that it can be employed both in the determined and underdetermined cases. Furthermore, the proposed algorithm is mainly based on the first-and second-order statistics and does not need the preprocessing so that the computational cost is significantly low. Simulation results show the superiority of the proposed algorithm over the existing methods and indicate that the proposed algorithm also performs well in the underdetermined case when the number of sensors is slightly less than that of sources.

show abstract

Section: The Proposed Algorithmmentioning

confidence: 99%