Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain

Bey, Abdeldjalil Aïssa El; Linh-Trung, Nguyen; Abed-Meraim, Karim; Belouchrani, Adel; Grenier, Yves

doi:10.1109/tsp.2006.888877

Cited by 173 publications

(175 citation statements)

References 18 publications

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“…However, after exploiting the sparsity of sources and under some assumptions, it can be solved successfully. Aissa-El-Bey et al [15] first introduced the subspace projection method for signal extraction supposing the number of signals appeared at any TF point was less than the number of sensors.…”

Section: Source Signals Extractionmentioning

confidence: 99%

“…However, even if the mixing matrix has been estimated, the original signals are still not easily recovered in the underdetermined condition because the model has more unknowns than equations. To solve the illconditioned problem, additional assumptions must be set, then sources can be extracted by minimum norm solution using p l -norm criterion [13], matrix diagonalization [14], and subspace method [15], etc.…”

Section: Introductionmentioning

confidence: 99%

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Underdetermined Blind Source Separation from Time-delayed Mixtures Based on Prior Information Exploitation

Zhang¹,

Yang²,

Guo³

et al. 2015

Journal of Electrical Engineering and Technology

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-Recently, many researches have been done to solve the challenging problem of Blind Source Separation (BSS) problems in the underdetermined cases, and the "Two-step" method is widely used, which estimates the mixing matrix first and then extracts the sources. To estimate the mixing matrix, conventional algorithms such as Single-Source-Points (SSPs) detection only exploits the sparsity of original signals. This paper proposes a new underdetermined mixing matrix estimation method for time-delayed mixtures based on the receiver prior exploitation. The prior information is extracted from the specific structure of the complex-valued mixing matrix, which is used to derive a special criterion to determine the SSPs. Moreover, after selecting the SSPs, Agglomerative Hierarchical Clustering (AHC) is used to automaticly cluster, suppress, and estimate all the elements of mixing matrix. Finally, a convex-model based subspace method is applied for signal separation. Simulation results show that the proposed algorithm can estimate the mixing matrix and extract the original source signals with higher accuracy especially in low SNR environments, and does not need the number of sources before hand, which is more reliable in the real non-cooperative environment.

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Section: Source Signals Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underdetermined Blind Source Separation from Time-delayed Mixtures Based on Prior Information Exploitation

Zhang¹,

Yang²,

Guo³

et al. 2015

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…However, such an approach requires prior knowledge of the source distributions. In contrast, sparseness-based methods solve the UBSS problem [12][13][14][15][16][17][18][19][20] without prior knowledge on the source distribution, by exploiting the sparseness of the non-stationary sources in the time-frequency domain. Roughly speaking, sparseness-based approaches [21] involve transforming the mixtures into an appropriate representation domain.…”

Section: Introductionmentioning

confidence: 99%

“…In the instantaneous mixture case, where each observation consists of a sum of sources with different signal http://asp.eurasipjournals.com/content/2012/1/169 intensity in presence of noise, the sparseness-based methods introduced in [12][13][14][15][16][17], among others, rely on parameters that are chosen empirically. The general question addressed in this article is then to what extent this empirical parameter choice can be by-passed thanks to statistical methods, specifically designed to cope with sparse representations.…”

Section: Introductionmentioning

confidence: 99%

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Contribution of statistical tests to sparseness-based blind source separation

Aziz-Sbaï

Bey

Pastor

2012

EURASIP J. Adv. Signal Process.

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We address the problem of blind source separation in the underdetermined mixture case. Two statistical tests are proposed to reduce the number of empirical parameters involved in standard sparseness-based underdetermined blind source separation (UBSS) methods. The first test performs multisource selection of the suitable time-frequency points for source recovery and is full automatic. The second one is dedicated to autosource selection for mixing matrix estimation and requires fixing two parameters only, regardless of the instrumented SNRs. We experimentally show that the use of these tests incurs no performance loss and even improves the performance of standard weak-sparseness UBSS approaches.

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Response‐only modal identification of structures using strong motion data

Ghahari

Abazarsa

Ghannad

et al. 2012

Earthq Engng Struct Dyn

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SUMMARY Dynamic characteristics of structures — viz. natural frequencies, damping ratios, and mode shapes — are central to earthquake‐resistant design. These values identified from field measurements are useful for model validation and health‐monitoring. Most system identification methods require input excitations motions to be measured and the structural response; however, the true input motions are seldom recordable. For example, when soil–structure interaction effects are non‐negligible, neither the free‐field motions nor the recorded responses of the foundations may be assumed as ‘input’. Even in the absence of soil–structure interaction, in many instances, the foundation responses are not recorded (or are recorded with a low signal‐to‐noise ratio). Unfortunately, existing output‐only methods are limited to free vibration data, or weak stationary ambient excitations. However, it is well‐known that the dynamic characteristics of most civil structures are amplitude‐dependent; thus, parameters identified from low‐amplitude responses do not match well with those from strong excitations, which arguably are more pertinent to seismic design. In this study, we present a new identification method through which a structure's dynamic characteristics can be extracted using only seismic response (output) signals. In this method, first, the response signals’ spatial time‐frequency distributions are used for blindly identifying the classical mode shapes and the modal coordinate signals. Second, cross‐relations among the modal coordinates are employed to determine the system's natural frequencies and damping ratios on the premise of linear behavior for the system. We use simulated (but realistic) data to verify the method, and also apply it to a real‐life data set to demonstrate its utility. Copyright © 2012 John Wiley & Sons, Ltd.

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Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain

Cited by 173 publications

References 18 publications

Underdetermined Blind Source Separation from Time-delayed Mixtures Based on Prior Information Exploitation

Underdetermined Blind Source Separation from Time-delayed Mixtures Based on Prior Information Exploitation

Contribution of statistical tests to sparseness-based blind source separation

Response‐only modal identification of structures using strong motion data

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