A combined approach of array processing and independent component analysis for blind separation of acoustic signals

Asano, Futoshi; Ikeda, Shiro; Ogawa, Mitsuhiro; Asoh, Hideki; Kitawaki, Nobuhiko

doi:10.1109/icassp.2001.940210

Cited by 48 publications

(40 citation statements)

References 4 publications

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“…The objective function usually consists of kurtosis, negative entropy, and mutual information. And the optimization algorithm usually includes the gradient descent algorithm and the fast fixed-point algorithm [14]. According to the central limit theory, the distribution of independent random variables tends to Gauss distribution under certain conditions.…”

Section: Ica Solution Methodsmentioning

confidence: 99%

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Near-infrared Spectrum Detection of Fish Oil DHA Content Based on Empirical Mode Decomposition and Independent Component Analysis

Jian-Hua¹

2014

JFNR

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The near infrared (NIR) spectrum of fish oil is often very weak, and part of the peaks are submerged in the noise and are difficult to distinguish especially when NIR spectrum is applied to component analysis. A new method is proposed to get the pretreatment of NIR spectrum, which combines empirical mode decomposition (EMD) with independent component analysis (ICA). The principle and steps of method are given and its de-noising effect is evaluated by some parameters. With experiment, it is indicated that the de-noising effect of fish oil spectrum is slightly better than that of Wavelet and EMD method. After de-noising, the noise has been almost completely inhibited and the characteristic peak of spectrum is preserved well. SNR reaches 34.613. RMSE is only 0.00257 and SR reaches 0.99976. The horizontal feature and vertical features of spectrum are retained well. Then the fish oil DHA content is calculated based on the de-noised spectrum. The correlation ratio of the prediction set is improved to 0.9887 from 0.9682, and the RMSEP is reduced to 0.0308 from 0.0572. These improved that the proposed method is effective to get the pretreatment of NIR spectrum and improves the accuracy of near-infrared spectrum detection of fish oil DHA content.

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Section: Ica Solution Methodsmentioning

confidence: 99%

“…This algorithm has some good advantages such as: robustness and faster convergence rate. The algorithm is follows [13,14,15]:…”

Section: Ica Solution Methodsmentioning

confidence: 99%

Near-infrared Spectrum Detection of Fish Oil DHA Content Based on Empirical Mode Decomposition and Independent Component Analysis

Jian-Hua¹

2014

JFNR

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“…In practical situations, SO and FO statistics have to be estimated from components of x(k). If components are stationary and ergodic, sample statistics may be used to estimate (2) and (4). Nevertheless, if sources are cyclostationary, cycloergodic, potentially non zero-mean, SO and FO continuous time average statistics have to be used instead of (2) and (4), such as…”

Section: Statistical Estimationmentioning

confidence: 99%

“…ICAR, a tool for Blind Source Separation using Fourth Order Statistics only I. INTRODUCTION I NDEPENDENT Component Analysis (ICA) plays an important role in various application areas, including radiocommunications, radar, sonar, seismology, radio astronomy, data analysis, speech and medical diagnosis [4] [20]. In digital radiocommunications contexts for instance, if some sources are received by an array of sensors, and if the channel delay spread associated with the different sensors is significantly smaller than the symbol durations for each source, a static mixture of complex sources is observed on the sensors.…”

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confidence: 99%

ICAR: a tool for blind source separation using fourth-order statistics only

Albera

Férreol

Chevalier

et al. 2005

IEEE Trans. Signal Process.

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The problem of blind separation of overdetermined mixtures of sources, that is, with fewer sources than (or as many sources as) sensors, is addressed in this paper. A new method, named ICAR (Independent Component Analysis using Redundancies in the quadricovariance), is proposed in order to process complex data. This method, without any whitening operation, only exploits some redundancies of a particular quadricovariance matrix of the data. Computer simulations demonstrate that ICAR offers in general good results and even outperforms classical methods in several situations: ICAR (i) succeeds in separating sources with low signal to noise ratios, (ii) does not require sources with different SO or/and FO spectral densities, (iii) is asymptotically not affected by the presence of a Gaussian noise with unknown spatial correlation, (iv) is not sensitive to an over estimation of the number of sources.

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“…In a real room environment, however, sounds are mixed in a convolutive manner with reverberations, and long reverberations make the problem difficult. One of the major methods to cope with reverberations is frequency-domain BSS [4][5][6][7]. In this approach, a convolutive mixture in the time domain is converted into multiple instantaneous mixtures in the frequency domain, and ICA is applied to the instantaneous mixture in every frequency bin.…”

Section: Introductionmentioning

confidence: 99%

Polar coordinate based nonlinear function for frequency-domain blind source separation

Sawada

Mukai

Araki

et al. 2002

IEEE International Conference on Acoustics Speech and Signal Processing

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This paper presents a new type of nonlinear function for independent component analysis to process complex-valued signals, which is used in frequency-domain blind source separation. The new function is based on the polar coordinates of a complex number, whereas the conventional one is based on the Cartesian coordinates. The new function is derived from the probability density function of frequencydomain signals that are assumed to be independent of the phase. We show that the difference between the two types of functions is in the assumed densities of independent components. Experimental results for separating speech signals show that the new nonlinear function behaves better than the conventional one.

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A combined approach of array processing and independent component analysis for blind separation of acoustic signals

Cited by 48 publications

References 4 publications

Near-infrared Spectrum Detection of Fish Oil DHA Content Based on Empirical Mode Decomposition and Independent Component Analysis

Near-infrared Spectrum Detection of Fish Oil DHA Content Based on Empirical Mode Decomposition and Independent Component Analysis

ICAR: a tool for blind source separation using fourth-order statistics only

Polar coordinate based nonlinear function for frequency-domain blind source separation

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