4,039 research outputs found
New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources
Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications
On asymptotics of ICA estimators and their performance indices
Independent component analysis (ICA) has become a popular multivariate
analysis and signal processing technique with diverse applications. This paper
is targeted at discussing theoretical large sample properties of ICA unmixing
matrix functionals. We provide a formal definition of unmixing matrix
functional and consider two popular estimators in detail: the family based on
two scatter matrices with the independence property (e.g., FOBI estimator) and
the family of deflation-based fastICA estimators. The limiting behavior of the
corresponding estimates is discussed and the asymptotic normality of the
deflation-based fastICA estimate is proven under general assumptions.
Furthermore, properties of several performance indices commonly used for
comparison of different unmixing matrix estimates are discussed and a new
performance index is proposed. The proposed index fullfills three desirable
features which promote its use in practice and distinguish it from others.
Namely, the index possesses an easy interpretation, is fast to compute and its
asymptotic properties can be inferred from asymptotics of the unmixing matrix
estimate. We illustrate the derived asymptotical results and the use of the
proposed index with a small simulation study
A Novel FastICA Method for the Reference-based Contrast Functions
This paper deals with the efficient optimization problem of Cumulant-based contrast criteria in the Blind Source Separation (BSS) framework, in which sources are retrieved by maximizing the Kurtosis contrast function. Combined with the recently proposed reference-based contrast schemes, a new fast fixed-point (FastICA) algorithm is proposed for the case of linear and instantaneous mixture. Due to its quadratic dependence on the number of searched parameters, the main advantage of this new method consists in the significant decrement of computational speed, which is particularly striking with large number of samples. The method is essentially similar to the classical algorithm based on the Kurtosis contrast function, but differs in the fact that the reference-based idea is utilized. The validity of this new method was demonstrated by simulations
New kurtosis optimization schemes for MISO equalization
International audienceThis 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 Expectation-Maximization (EM) method and with recent generalizations referred to as Minimization-Maximization (MM). 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
An Efficient Algorithm by Kurtosis Maximization in Reference-Based Framework
This paper deals with the optimization of kurtosis for complex-valued signals in the independent component analysis (ICA) framework, where source signals are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast schemes, a similar contrast function is put forward, based on which a new fast fixed-point (FastICA) algorithm is proposed. The new optimization method is similar in spirit to the former classical kurtosis-based FastICA algorithm but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. The performance of this new algorithm is confirmed through computer simulations
Blind Signal Separation for Digital Communication Data
to appear in EURASIP E-reference in Signal Processing, invited paper.International audienceBlind source separation, often called independent component analysis , is a main field of research in signal processing since the eightees. It consists in retrieving the components, up to certain indeterminacies, of a mixture involving statistically independent signals. Solid theoretical results are known; besides, they have given rise to performent algorithms. There are numerous applications of blind source separation. In this contribution, we particularize the separation of telecommunication sources. In this context, the sources stem from telecommunication devices transmitting at the same time in a given band of frequencies. The received data is a mixed version of all these sources. The aim of the receiver is to isolate (separate) the different contributions prior to estimating the unknown parameters associated with a transmitter. The context of telecommunication signals has the particularity that the sources are not stationary but cyclo-stationary. Now, in general, the standard methods of blind source separation assume the stationarity of the sources. In this contribution , we hence make a survey of the well-known methods and show how the results extend to cyclo-stationary sources
Independent Component Analysis Enhancements for Source Separation in Immersive Audio Environments
In immersive audio environments with distributed microphones, Independent Component Analysis (ICA) can be applied to uncover signals from a mixture of other signals and noise, such as in a cocktail party recording. ICA algorithms have been developed for instantaneous source mixtures and convolutional source mixtures. While ICA for instantaneous mixtures works when no delays exist between the signals in each mixture, distributed microphone recordings typically result various delays of the signals over the recorded channels. The convolutive ICA algorithm should account for delays; however, it requires many parameters to be set and often has stability issues. This thesis introduces the Channel Aligned FastICA (CAICA), which requires knowledge of the source distance to each microphone, but does not require knowledge of noise sources. Furthermore, the CAICA is combined with Time Frequency Masking (TFM), yielding even better SOI extraction even in low SNR environments. Simulations were conducted for ranking experiments tested the performance of three algorithms: Weighted Beamforming (WB), CAICA, CAICA with TFM. The Closest Microphone (CM) recording is used as a reference for all three. Statistical analyses on the results demonstrated superior performance for the CAICA with TFM. The algorithms were applied to experimental recordings to support the conclusions of the simulations. These techniques can be deployed in mobile platforms, used in surveillance for capturing human speech and potentially adapted to biomedical fields
A general algebraic algorithm for blind extraction of one source in a MIMO convolutive mixture
International audienceThe paper deals with the problem of blind source extraction from a MIMO convolutive mixture. We define a new criterion for source extraction which uses higher-order contrast functions based on so called reference signals. It generalizes existing reference-based contrasts. In order to optimize the new criterion, we propose a general algebraic algorithm based on best rank-1 tensor approximation. Computer simulations illustrate the good behavior and the interest of our algorithm in comparison with other approaches
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