Blind source recovery: algorithms for static and dynamic environments

Salam, F.M.A.; Erten, G.; Waheed, K.

doi:10.1109/ijcnn.2001.939479

Cited by 18 publications

(49 citation statements)

References 8 publications

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“…Unlike the transfer function models, the state-space provides an efficient internal description of a system. Moreover, there are various possible equivalent state space realizations for a system, and thus recovery of original sources can be achieved independent from (and even in the absence of) environment identifiability [5]. Further, The inverse for a state space representation is easily derived subject to the "invertibility" of the instantaneous relational mixing matrix between input-output.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the asymptotic convergence of the algorithm is slower as compared to the minimum phase systems, this can be attributed to the transients induced in the algorithm due to bi-directional nature of the algorithm [8] The state space notion provides a compact representation, which is capable of handling both time delayed and filtered versions of signals in an organized manner [5,6,7]. Unlike the transfer function models, the state-space provides an efficient internal description of a system.…”

Section: Introductionmentioning

confidence: 99%

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State space blind source recovery of non-minimum phase environments

Waheed

Salarn

The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002.

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The paper describes the use of the state space and the natural gradient for the demixing of sources mixed in a non-minimum phase convolutive environment. Non-minimum phase implies that some or all of the zeros of the mixing environment lie outside the unit circle, and as such the theoretical inverse or the requisite demixing system becomes unstable due to the presence of poles outside the unit circle. These unstable poles are required to cancel out the non-minimum phase transmission zeros of the environment. In order to avoid instability due to the existence of these poles outside the unit circle, the natural gradient algorithm may be derived with the constraint that the demixing system is a double sided FIR filter, i.e., instead of trying to determine the IIR inverse of the environment, we will approximate the inverse using an all zero non-causal filter. The use of the state space warrants that the derived framework is rich in structure while at the same time compact in representation. This results in derivation of update laws that can invariably handle most mixing scenarios. Some simulations illustrating the performance of the algorithm are also provided.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State space blind source recovery of non-minimum phase environments

Waheed

Salarn

The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002.

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“…The state space notion provides a compact representation, capable of handling both time delayed and filtered versions of signals in an organized manner [2,3,6]. Unlike the transfer function models of standard dynamic filters, the use of the state-space can result in several generalized, equivalent and efficient internal descriptions of a system.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of BSR algorithms strongly depends on the choice of an appropriate score function which appears as an element wise acting non-linearity on the output signals [2][3][4][5][6]. For a particular problem, the optimal score function depends on the distribution of the original source signals which are unknown in a blind scenario.…”

Section: Introductionmentioning

confidence: 99%

“…Blind Source Recovery (BSR) is an interesting autonomous (or unsupervised) stochastic adaptation problem that includes wellknown adaptive signal processing problems of multi-channel Blind Source Separation (BSS), Deconvolution (BSD) and Equalization (BSE) with several potential applications [2,3,6]. The BSR problem denotes recovering original sources from environments that may include convolution, transients, and even possible nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

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Blind source recovery using an adaptive generalized Gaussian score function

Waheed

Salam

The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002.

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The paper discusses Blind Source Recovery (BSR) of minimum phase and non-minimum phase mixtures of multiple source distributions using an adaptive score function. This proposed parametric score function is derived from the generalized gaussian distribution model. An adaptive algorithm to determine the tuning parameter for the proposed score function using the batch kurtosis of BSR output is also presented. The primary advantage of the proposed framework is that it renders the adaptive estimation of the demixing network to be completely blind. No a priori information about the distribution structure of the original sources is required. Simulation examples verifying the proposed framework are also presented.

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Stability and Control of Dynamical Systems with Applications

Liu¹,

Antsaklis²

2003

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Blind source recovery: algorithms for static and dynamic environments

Cited by 18 publications

References 8 publications

State space blind source recovery of non-minimum phase environments

State space blind source recovery of non-minimum phase environments

Blind source recovery using an adaptive generalized Gaussian score function

Stability and Control of Dynamical Systems with Applications

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