238 research outputs found
DimDim a l'aula
Resultat del grup de treball CIFE-GI "Incorporació de les TIC a la docència" essent un espai d'intercanvi d'experiències i de promoció de la transferència d'activitats docents dutes a terme amb el suport de les TIC.Seguiment de la classe mitjaçant DimDi
Processing, traitement, processament
Un títol una mica estrany per a una lliçó inaugural, però que té una explicació
simple: d’entrada tres paraules, que ens marcaran els tres apartats
en què he dividit aquesta lliçó. I després tres idiomes, que em serviran per
centrar cada un dels tres apartats i enllaçar-los entre ells, per fer de les
parts un tot únic i coherent
Estimación de funciones no lineales en mezclas post-no lineales
This paper proposes a new method for blindly inverting a nonlinear mapping which transforms a sum of random variables. This is the case of post-nonlinear (PNL) source separation mixtures. The importance of the method is based on the fact that it permits to decouple the estimation of the nonlinear part from the estimation of the linear one. Only the nonlinear part is inverted, without considering on the linear part. Hence the initial problem is transformed into a linear one that can then be solved with any convenient linear algorithm. The method is compared with other existing algorithms for blindly approximating nonlinear mappings. Experiments show that the proposed algorithm outperforms the results obtained with other algorithms and give a reasonably good linearized dat
On cumulant techniques in speech processing
This paper analyzes applications of cumulant analysis in
speech processing. A special focus is made on different second-order
statistics. A dominant role is played by an integral representation for
cumulants by means of integrals involving cyclic products of kernels
Post-Nonlinear Mixtures and Beyond
Although sources in general nonlinear mixturm arc not separable iising only statistical
independence, a special and realistic case of nonlinear mixtnres, the post nonlinear
(PNL) mixture is separable choosing a suited separating system. Then, a natural approach is
based on the estimation of tho separating Bystem parameters by minimizing an indcpendence
criterion, like estimated mwce mutual information. This class of methods requires higher
(than 2) order statistics, and cannot separate Gaarsian sources. However, use of [weak) prior,
like source temporal correlation or nonstationarity, leads to other source separation Jgw
rithms, which are able to separate Gaussian sourra, and can even, for a few of them, works
with second-order statistics. Recently, modeling time correlated s011rces by Markov models,
we propose vcry efficient algorithms hmed on minimization of the conditional mutual information.
Currently, using the prior of temporally correlated sources, we investigate the fesihility
of inverting PNL mixtures with non-bijectiw non-liacarities, like quadratic functions. In this
paper, we review the main ICA and BSS results for riunlinear mixtures, present PNL models
and algorithms, and finish with advanced resutts using temporally correlated snu~s
Blind channel deconvolution of real world signals using source separation techniques
In this paper we present a method for blind deconvolution of linear
channels based on source separation techniques, for real word signals. This
technique applied to blind deconvolution problems is based in exploiting not
the spatial independence between signals but the temporal independence between
samples of the signal. Our objective is to minimize the mutual information
between samples of the output in order to retrieve the original signal. In
order to make use of use this idea the input signal must be a non-Gaussian i.i.d.
signal. Because most real world signals do not have this i.i.d. nature, we will
need to preprocess the original signal before the transmission into the channel.
Likewise we should assure that the transmitted signal has non-Gaussian statistics
in order to achieve the correct function of the algorithm. The strategy used
for this preprocessing will be presented in this paper. If the receiver has the inverse
of the preprocess, the original signal can be reconstructed without the
convolutive distortion
Speaker recognition improvement using blind inversion of distortions
In this paper we propose the inversion of nonlinear
distortions in order to improve the recognition rates of a
speaker recognizer system. We study the effect of
saturations on the test signals, trying to take into account
real situations where the training material has been recorded
in a controlled situation but the testing signals present some
mismatch with the input signal level (saturations). The
experimental results shows that a combination of several
strategies can improve the recognition rates with saturated
test sentences from 80% to 89.39%, while the results with
clean speech (without saturation) is 87.76% for one
microphone
Nonlinear prediction based on score function
The linear prediction coding of speech is based in the
assumption that the generation model is autoregresive. In
this paper we propose a structure to cope with the
nonlinear effects presents in the generation of the speech
signal. This structure will consist of two stages, the first
one will be a classical linear prediction filter, and the
second one will model the residual signal by means of
two nonlinearities between a linear filter. The coefficients
of this filter are computed by means of a gradient search
on the score function. This is done in order to deal with
the fact that the probability distribution of the residual
signal still is not gaussian. This fact is taken into account
when the coefficients are computed by a ML estimate.
The algorithm based on the minimization of a high-order
statistics criterion, uses on-line estimation of the residue
statistics and is based on blind deconvolution of Wiener
systems [1]. Improvements in the experimental results
with speech signals emphasize on the interest of this
approach
Source separation techniques applied to linear prediction
The prediction filters are well known models for signal
estimation, in communications, control and many others
areas. The classical method for deriving linear
prediction coding (LPC) filters is often based on the
minimization of a mean square error (MSE).
Consequently, second order statistics are only required,
but the estimation is only optimal if the residue is
independent and identically distributed (iid) Gaussian.
In this paper, we derive the ML estimate of the
prediction filter. Relationships with robust estimation of
auto-regressive (AR) processes, with blind
deconvolution and with source separation based on
mutual information minimization are then detailed. The
algorithm, based on the minimization of a high-order
statistics criterion, uses on-line estimation of the residue
statistics. Experimental results emphasize on the
interest of this approach
Quasi-Nonparametric Blind Inversion of Wiener Systems
An e cient procedure for the blind inversion of a nonlinear Wiener system is proposed. We proved that the
problem can be expressed as a problem of blind source separation in nonlinear mixtures, for which a solution
has been recently proposed. Based on a quasi-nonparametric relative gradient descent, the proposed algorithm
can perform e ciently even in the presence of hard distortions
- …