A blind source separation framework for detecting CPM sources mixed by a convolutive MIMO filter

Castella, Marc; Bianchi, Pascal; Chevreuil, Antoine; Pesquet, Jean-Christophe

doi:10.1016/j.sigpro.2005.06.018

Cited by 13 publications

(18 citation statements)

References 24 publications

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“…Among the possible approaches, the source signals can be either separated simultaneously [3]- [6] or extracted one by one by optimizing for each a multi-input/single-output (MISO) separating criterion. In this paper, we consider the latter approach, which is often combined with a deflation procedure to extract all the sources [7]- [9].…”

Section: A Generalitiesmentioning

confidence: 99%

“…Nevertheless some signals such as those stemming from a continuous phase modulation (CPM) may be non i.i.d. [7]. An interesting question thus consists in knowing whether Proposition 1 remains true without Assumption A3.…”

Section: Commentsmentioning

confidence: 99%

“…We consider FIR separators of given length D. The conditions under which such a separator exists can be found in [7], [11]. The separating filter w is then replaced by the following 1 × QD vector which concatenates the vectors of the separator impulse response:…”

Section: B Implementationmentioning

confidence: 99%

“…The separating FIR separator has been searched with length D = N (L − 1) = 6. An inverse of length D exists generically for the FIR randomly chosen mixing system (see [7], [11] and references therein for details).…”

Section: A Performance Of the Algorithms 1) Validity And Separation mentioning

confidence: 99%

“…However, the general scalar filtering ambiguity that generally remains when separating non i.i.d. sources has been characterized in the case of a kurtosis-based contrast function [7], [24], [25]. It is hence interesting to try in simulation our optimization method with CPM sources.…”

Section: ) Non Iid Sources: Continuous Phase Modulationmentioning

confidence: 99%

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New Kurtosis Optimization Schemes for MISO Equalization

Castella

Moreau

2012

IEEE Trans. Signal Process.

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Abstract-This paper deals with efficient optimization of cumulant based contrast functions. Such a problem occurs in the blind source separation framework, where contrast functions are criteria to be maximized in order to retrieve the sources. More precisely, we focus on the extraction of one source signal and our method applies in deflation approaches, where the sources are extracted one by one.We propose new methods to maximize the kurtosis contrast function. These methods are intermediate between a gradient and an iterative "fixed-point" optimization of so-called reference contrasts. They rely on iterative updates of the parameters which monotonically increase the contrast function value: we point out the strong similarity with the EM (Expectation-Maximization) method and with recent generalizations referred to as MM (Minimization-Maximization). We also prove the global convergence of the algorithm to a stationary point. Simulations confirm the convergence of our methods to a separating solution. They also show experimentally that our methods have a much lower computational cost than former classical optimization methods. Finally, simulations suggest that the methods remain valid under weaker conditions than those required for proving convergence.

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Section: A Generalitiesmentioning

confidence: 99%

Section: Commentsmentioning

confidence: 99%

Section: B Implementationmentioning

confidence: 99%

Section: A Performance Of the Algorithms 1) Validity And Separation mentioning

confidence: 99%

Section: ) Non Iid Sources: Continuous Phase Modulationmentioning

confidence: 99%

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New Kurtosis Optimization Schemes for MISO Equalization

Castella

Moreau

2012

IEEE Trans. Signal Process.

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A Survey of Kurtosis Optimization Schemes for MISO Source Separation and Equalization

Castella¹,

Moreau²,

Zarzoso³

2013

Advances in Heuristic Signal Processing and Applications

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A new and fast approach towards sEMG decomposition

Gligorijević

Dijk

Mijović

et al. 2013

Med Biol Eng Comput

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The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using shapes detected on these channels, the hierarchical clustering algorithm as reported by Quian Quiroga et al. (Neural Comput 16:1661-1687, 2004) is extended for multichannel data in order to obtain the motor unit action potential (MUAP) signatures. After this first step, more motor unit firings are obtained using the extracted signatures by a novel demixing technique. In this demixing stage, we propose a time-efficient solution for the general convolutive system that models the motor unit firings on the HD-sEMG grid. We constrain this system by using the extracted signatures as prior knowledge and reconstruct the firing patterns in a computationally efficient way. The algorithm performance is successfully verified on simulated data containing up to 20 different MUAP signatures. Moreover, we tested the method on real low contraction recordings from the lateral vastus leg muscle by comparing the algorithm's output to the results obtained by manual analysis of the data from two independent trained operators. The proposed method showed to perform about equally successful as the operators.

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A blind source separation framework for detecting CPM sources mixed by a convolutive MIMO filter

Cited by 13 publications

References 24 publications

New Kurtosis Optimization Schemes for MISO Equalization

New Kurtosis Optimization Schemes for MISO Equalization

A Survey of Kurtosis Optimization Schemes for MISO Source Separation and Equalization

A new and fast approach towards sEMG decomposition

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