185 research outputs found
Wavelet Domain Image Separation
In this paper, we consider the problem of blind signal and image separation
using a sparse representation of the images in the wavelet domain. We consider
the problem in a Bayesian estimation framework using the fact that the
distribution of the wavelet coefficients of real world images can naturally be
modeled by an exponential power probability density function. The Bayesian
approach which has been used with success in blind source separation gives also
the possibility of including any prior information we may have on the mixing
matrix elements as well as on the hyperparameters (parameters of the prior laws
of the noise and the sources). We consider two cases: first the case where the
wavelet coefficients are assumed to be i.i.d. and second the case where we
model the correlation between the coefficients of two adjacent scales by a
first order Markov chain. This paper only reports on the first case, the second
case results will be reported in a near future. The estimation computations are
done via a Monte Carlo Markov Chain (MCMC) procedure. Some simulations show the
performances of the proposed method. Keywords: Blind source separation,
wavelets, Bayesian estimation, MCMC Hasting-Metropolis algorithm.Comment: Presented at MaxEnt2002, the 22nd International Workshop on Bayesian
and Maximum Entropy methods (Aug. 3-9, 2002, Moscow, Idaho, USA). To appear
in Proceedings of American Institute of Physic
Using state space differential geometry for nonlinear blind source separation
Given a time series of multicomponent measurements of an evolving stimulus,
nonlinear blind source separation (BSS) seeks to find a "source" time series,
comprised of statistically independent combinations of the measured components.
In this paper, we seek a source time series with local velocity cross
correlations that vanish everywhere in stimulus state space. However, in an
earlier paper the local velocity correlation matrix was shown to constitute a
metric on state space. Therefore, nonlinear BSS maps onto a problem of
differential geometry: given the metric observed in the measurement coordinate
system, find another coordinate system in which the metric is diagonal
everywhere. We show how to determine if the observed data are separable in this
way, and, if they are, we show how to construct the required transformation to
the source coordinate system, which is essentially unique except for an unknown
rotation that can be found by applying the methods of linear BSS. Thus, the
proposed technique solves nonlinear BSS in many situations or, at least,
reduces it to linear BSS, without the use of probabilistic, parametric, or
iterative procedures. This paper also describes a generalization of this
methodology that performs nonlinear independent subspace separation. In every
case, the resulting decomposition of the observed data is an intrinsic property
of the stimulus' evolution in the sense that it does not depend on the way the
observer chooses to view it (e.g., the choice of the observing machine's
sensors). In other words, the decomposition is a property of the evolution of
the "real" stimulus that is "out there" broadcasting energy to the observer.
The technique is illustrated with analytic and numerical examples.Comment: Contains 14 pages and 3 figures. For related papers, see
http://www.geocities.com/dlevin2001/ . New version is identical to original
version except for URL in the bylin
An adaptive stereo basis method for convolutive blind audio source separation
NOTICE: this is the authorâs version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [71, 10-12, June 2008] DOI:neucom.2007.08.02
A general framework for online audio source separation
We consider the problem of online audio source separation. Existing
algorithms adopt either a sliding block approach or a stochastic gradient
approach, which is faster but less accurate. Also, they rely either on spatial
cues or on spectral cues and cannot separate certain mixtures. In this paper,
we design a general online audio source separation framework that combines both
approaches and both types of cues. The model parameters are estimated in the
Maximum Likelihood (ML) sense using a Generalised Expectation Maximisation
(GEM) algorithm with multiplicative updates. The separation performance is
evaluated as a function of the block size and the step size and compared to
that of an offline algorithm.Comment: International conference on Latente Variable Analysis and Signal
Separation (2012
Sparse Coding on Stereo Video for Object Detection
Deep Convolutional Neural Networks (DCNN) require millions of labeled
training examples for image classification and object detection tasks, which
restrict these models to domains where such datasets are available. In this
paper, we explore the use of unsupervised sparse coding applied to stereo-video
data to help alleviate the need for large amounts of labeled data. We show that
replacing a typical supervised convolutional layer with an unsupervised
sparse-coding layer within a DCNN allows for better performance on a car
detection task when only a limited number of labeled training examples is
available. Furthermore, the network that incorporates sparse coding allows for
more consistent performance over varying initializations and ordering of
training examples when compared to a fully supervised DCNN. Finally, we compare
activations between the unsupervised sparse-coding layer and the supervised
convolutional layer, and show that the sparse representation exhibits an
encoding that is depth selective, whereas encodings from the convolutional
layer do not exhibit such selectivity. These result indicates promise for using
unsupervised sparse-coding approaches in real-world computer vision tasks in
domains with limited labeled training data
Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments
El uso eficiente del espectro se ha convertido en un ĂĄrea de investigaciĂłn activa, debido a la escasez de este recurso y a su subutilizaciĂłn. En un escenario en el que el espectro es un recurso compartido como en la radio cognitiva (CR), los espacios sin uso dentro de las bandas de frecuencias con licencia podrĂan ser detectados y posteriormente utilizados por un usuario secundario a travĂ©s de tĂ©cnicas de detecciĂłn y sensado del espectro. Generalmente, estas tĂ©cnicas de detecciĂłn se utilizan a partir de un conocimiento previo de las caracterĂsticas de canal. En el presente trabajo se propone un enfoque de detecciĂłn ciega del espectro basado en anĂĄlisis de componentes independientes (ICA) y anĂĄlisis de espectro singular (SSA). La tĂ©cnica de detecciĂłn se valida a travĂ©s de simulaciĂłn, y su desempeño se compara con metodologĂas propuestas por otros autores en la literatura. Los resultados muestran que el sistema propuesto es capaz de detectar la mayorĂa de las fuentes con bajo consumo de tiempo, un aspecto que cabe resaltar para aplicaciones en lĂnea con exigencias de tiempo.The efficient use of spectrum has become an active research area, due to its scarcity and underutilization. In a spectrum sharing scenario as Cognitive Radio (CR), the vacancy of licensed frequency bands could be detected by a secondary user through spectrum sensing techniques. Usually, this sensing approaches are performed with a priori knowledge of the channel features. In the present work, a blind spectrum sensing approach based on Independent Component Analysis and Singular Spectrum Analysis is proposed. The approach is tested and compared with other outcomes. Results show that the proposed scheme is capable of detect most of the sources with low time consumption, which is a remarkable aspect for online applications with demanding time issues
On an argument of J.--F. Cardoso dealing with perturbations of joint diagonalizers
B. Afsari has recently proposed a new approach to the matrix joint
diagonalization, introduced by J.--F. Cardoso in 1994, in order to investigate
the independent component analysis and the blind signal processing in a wider
prospective. Delicate notions of linear algebra and differential geometry are
involved in the works of B. Afsari and the present paper continues such a line
of research, focusing on a theoretical condition which has significant
consequences in the numerical applications.Comment: 9 pages; the published version contains significant revisions
(suggested by the referees
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