Noisy Component Extraction (NoiCE)

Leong, Wai Yie; Mandic, Danilo P.

doi:10.1109/tcsi.2009.2024988

Cited by 13 publications

(10 citation statements)

References 24 publications

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“…It must be mentioned that the interesting information is seldom measured in isolation and is generally mixed with other ongoing background activity and additive noise [1][2][3][4]12,13]. To verify the efficiency and robustness of our algorithm, we conduct a series of computer simulation experiments on biomedical signals.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Therefore, a very intuitive and important principle of component estimation is maximum/minimum non-Gaussianity. As an important non-Gaussianity measuring index, the normalized kurtosis has been widely utilized as the objective function for the BSS/BSE problem [1,2,4,5,[10][11][12][13][14]. Despite being theoretically well justified, a major problem with existing normalized kurtosis-based methods is that a vast majority of previous research has been conducted with the assumption of no additive noise [4,5,10,11], a condition that is not realistic for most real-world sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Despite being theoretically well justified, a major problem with existing normalized kurtosis-based methods is that a vast majority of previous research has been conducted with the assumption of no additive noise [4,5,10,11], a condition that is not realistic for most real-world sensors. Recently, a few source extraction algorithms have been proposed based on normalized kurtosis to extract a desired source signal from noisy mixtures [12][13][14]. However, when we have prior knowledge that the normalized kurtosis of the desired signal generally lies in a specific value range, whereas other unwanted source signals do not belong to this range, it seems that no such existing BSE algorithms can be utilized for noisy cases.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A robust extraction algorithm for biomedical signals from noisy mixtures

Zhao

Liu

Wang

2011

Front. Comput. Sci. China

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Blind source extraction (BSE) is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extraction performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A robust extraction algorithm for biomedical signals from noisy mixtures

Zhao

Liu

Wang

2011

Front. Comput. Sci. China

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show abstract

“…(2) Where w is a column vector and y(t) is a recovered source signal up to a scalar. According to the Central Limit Theorem, the distribution of a sum of independent random variables tends to a Gaussian distribution [8][9][10]. From (2) the recovered source signal usually has a distribution closer to Gaussian than any one source signal and becomes least Gaussian when it equals one source signal.…”

Section: Tt Y T W X T W As T mentioning

confidence: 99%

“…In most cases, someone is not completely blind with signal mixtures [10][11][12]. In other words, he can know some prior information about the desired source signal in advance.…”

Section: Introductionmentioning

confidence: 99%

Flexible Constrained Techniques for Source Separation

Zhao¹

2017

dtetr

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Traditional blind source separation (BSS) technique aims to recover all source signals from their mixtures simultaneously. In many practical situations, someone is only interested in specific source signal. Here prior information called reference signal is introduced to guide signal separating process. As a result, a flexible constrained algorithm is deduced for extraction of the desired source signal exclusively. Computer simulations illustrate its validity and usefulness.

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Blind Nonparametric Determined and Underdetermined Signal Extraction Algorithm for Dependent Source Mixtures

Wang

et al. 2015

Intelligent Computing Theories and Methodologies

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Abstract. Blind extraction or separation statistically independent source signals from linear mixtures have been well studied in the last two decades by searching for local extrema of certain objective functions, such as nonGaussianity (NG) measure. Blind source extraction (BSE) algorithm from underdetermined linear mixtures of the statistically dependent source signals is derived using nonparametric NG measure in this paper. After showing that maximization of the NG measure can also separate or extract the statistically weak dependent source signals, the nonparametric NG measure is defined by statistical distances between different source signals distributions based on the cumulative density function (CDF) instead of traditional probability density function (PDF), which can be estimated by the quantiles and order statistics using the L 2 norm efficiently. The nonparametric NG measure can be optimized by a deflation procedure to extract or separate the dependent source signals. Simulation results for synthesis and real world data show that the proposed nonparametric extraction algorithm can extract the dependent signals and yield ideal performance.

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Noisy Component Extraction (NoiCE)

Cited by 13 publications

References 24 publications

A robust extraction algorithm for biomedical signals from noisy mixtures

A robust extraction algorithm for biomedical signals from noisy mixtures

Flexible Constrained Techniques for Source Separation

Blind Nonparametric Determined and Underdetermined Signal Extraction Algorithm for Dependent Source Mixtures

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