Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions

Peng, Dezhong; Xiang, Yong

doi:10.1016/j.dsp.2009.08.014

Cited by 38 publications

(9 citation statements)

References 30 publications

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“…We choose the maximum number of iterations to be only 50 iterations. We investigate the performance of the proposed UBSS approach in the above mentioned cases by comparing its results with the results of approaches in Snoussi and Idier (2006) [18], Peng and Xiang (2010) [19], and S. Sun et al (2012) [20]. Here, the simulation of the separation of sparse and Gaussian signals is provided followed by some discussions.…”

Section: Experiments and Simulation Resultsmentioning

confidence: 94%

“…T) _ �IIY(·) _ . TI1 2 ost Fro a J s j -2 ] a J s j Fro ' (9) Forj = 1,2, ... , J The optimality conditions for the set of cost functions (9) can be defined as aj (9 \7aj Cost ��o (Y(j)llajsJ) = 0 (10)…”

Section: B the Initialization Technique For Source Signzas Estimationmentioning

confidence: 99%

“…Consequently, recovering the source signals by multiplying the observable data mixtures by the pseudo inverse of the mixing matrix cannot be used. This makes recovering the source signals a difficult and very challenging task [9]. In practical terms, the overdetermined mixture assumption does not always hold.…”

Section: Introductionmentioning

confidence: 99%

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Blind separation of underdetermined mixtures with additive white and pink noises

Alshabrawy

Hassanien

Awad

et al. 2013

13th International Conference on Hybrid Intelligent Systems (HIS 2013)

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This paper presents an approach for underdeter mined blind source separation in the case of additive Gaussian white noise and pink noise. Likewise, the proposed approach is applicable in the case of separating I + 3 sources from I mixtures with additive two kinds of noises. This situation is more challenging and suitable to practical real world problems. Moreover, unlike to some conventional approaches, the sparsity conditions are not imposed. Firstly, the mixing matrix is estimated based on an algorithm that combines short time Fourier transform and rough-fuzzy clustering. Then, the mixed signals are normalized and the source signals are recovered using modified Gradient descent Local Hierarchical Alternat ing Least Squares Algorithm exploiting the mixing matrix obtained from the previous step as an input and initialized by multiplicative algorithm for matrix factorization based on alpha divergence. The experiments and simulation resultsshow that the proposed approach can separate I + 3 source signals from I mixed signals, and it has superior evaluation performance compared to some conventional approaches.

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Section: Experiments and Simulation Resultsmentioning

confidence: 94%

Section: B the Initialization Technique For Source Signzas Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Blind separation of underdetermined mixtures with additive white and pink noises

Alshabrawy

Hassanien

Awad

et al. 2013

13th International Conference on Hybrid Intelligent Systems (HIS 2013)

View full text Add to dashboard Cite

show abstract

“…The spatial image of the sources can be modeled as realization of zero-mean proper complex distribution , ,~( 0, , , ( ) ). (9) where ( , Σ) is proper complex Gaussian distribution [26] and its probability density function (pdf) can be expressed as…”

Section: Source Modelmentioning

confidence: 99%

“…In source separation it is more realistic to consider the effect of the surrounding environment such as reflection of the sources. To address this issue, researchers have considered convolutive mixtures [1][2][3][4][5][6][7] instead of the instantaneous mixture [8][9][10][11]. However, the convolutive mixture is modeled under the narrowband approximation [4] that is not valid when the mixing filter length is greater than the Short-Time Fourier Transform (STFT) windows length, which is the case of the reverberant environment.…”

Section: Introductionmentioning

confidence: 99%

Reverberant signal separation using optimized complex sparse nonnegative tensor deconvolution on spectral covariance matrix

Woo

Dlay

Al-Tmeme

et al. 2018

Digital Signal Processing

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In this paper, an optimized complex nonnegative tensor factor 2D deconvolution (CNTF2D) is proposed to separate the sources that have been mixed in an underdetermined reverberant environment. Unlike conventional methods, the proposed model decomposition is performed directly on the statistics in the form of spectral covariance matrix instead of the data itself (i.e. the mixed signal). For faster convergence the model is adapted under the hybrid framework of the generalized expectation maximization and multiplicative update algorithms. This paper also proposes a solution to the issue of optimizing the model order i.e., number of components and convolutive parameters in the CNTF2D model. To this end, a latent-observation model based on Gamma-Exponential process is developed. In addition, the proposed Gamma-Exponential process can be used to initialize the parameterization of the CNTF2D. The proposed algorithm encodes a set of variable sparsity parameters derived from the Gibbs distribution. This permits a stable update and optimizes the CNTF2D with the correct degree of sparseness in the time-frequency domain. Experimental results on the underdetermined reverberant mixing environment have shown that the proposed algorithm is effective at separating the mixture with an average signal-to-distortion ratio of 2.5dB.

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Estimation of the complex‐valued mixing matrix by single‐source‐points detection with less sensors than sources

Shen

Wang

et al. 2011

Trans. Emerg. Telecomms. Techs.

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This paper essentially considers the direction‐of‐arrival (DOA) estimation of far‐field source signals in the underdetermined blind separation, where the mixing matrix is complex‐valued. By distinguishing single‐source‐points (SSPs) from multi‐source‐points in the time‐frequency domain, a novel estimation algorithm is proposed based on the detection of SSPs, where only single source contributes and samples all correspond to one of the mixing column vectors. To further enhance the estimation accuracy, a modified version of the proposed algorithm is also presented, which utilizes the probability density distribution of the already detected SSPs and reselects those reliable SSPs with most likely probabilities. Finally, the mixing matrix as well as the DOAs can be obtained by performing the K‐means clustering algorithm on samples at selected SSPs. The results of numerical simulations validate the efficiency of both the two estimation algorithms. One of the outstanding superiorities for the SSPs‐based idea is that it can distinguish multiple DOAs using only two sensors; the other is that it relaxes the signal sparsity requirement to allow SSPs to merely occur at a small number of time‐frequency locations. Copyright © 2011 John Wiley & Sons, Ltd.

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Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions

Cited by 38 publications

References 30 publications

Blind separation of underdetermined mixtures with additive white and pink noises

Blind separation of underdetermined mixtures with additive white and pink noises

Reverberant signal separation using optimized complex sparse nonnegative tensor deconvolution on spectral covariance matrix

Estimation of the complex‐valued mixing matrix by single‐source‐points detection with less sensors than sources

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