97,189 research outputs found
Enhanced independent vector analysis for speech separation in room environments
PhD ThesisThe human brain has the ability to focus on a desired sound source in the presence
of several active sound sources. The machine based method lags behind in mimicking
this particular skill of human beings. In the domain of digital signal processing this
problem is termed as the cocktail party problem. This thesis thus aims to further
the eld of acoustic source separation in the frequency domain based on exploiting
source independence. The main challenge in such frequency domain algorithms is the
permutation problem. Independent vector analysis (IVA) is a frequency domain blind
source separation algorithm which can theoretically obviate the permutation problem
by preserving the dependency structure within each source vector whilst eliminating
the dependency between the frequency bins of di erent source vectors. This thesis in
particular focuses on improving the separation performance of IVA algorithms which
are used for frequency domain acoustic source separation in real room environments.
The source prior is crucial to the separation performance of the IVA algorithm as it
is used to model the nonlinear dependency structure within the source vectors. An
alternative multivariate Student's t distribution source prior is proposed for the IVA
algorithm as it is known to be well suited for modelling certain speech signals due to
its heavy tail nature. Therefore the nonlinear score function that is derived from the
proposed Student's t source prior can better model the dependency structure within the
frequency bins and thereby enhance the separation performance and the convergence
speed of the IVA and the Fast version of the IVA (FastIVA) algorithms.
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A novel energy driven mixed Student's t and the original super Gaussian source prior
is also proposed for the IVA algorithms. As speech signals can be composed of many
high and low amplitude data points, therefore the Student's t distribution in the mixed
source prior can account for the high amplitude data points whereas the original su-
per Gaussian distribution can cater for the other information in the speech signals.
Furthermore, the weight of both distributions in the mixed source prior can be ad-
justed according to the energy of the observed mixtures. Therefore the mixed source
prior adapts the measured signals and further enhances the performance of the IVA
algorithm.
A common approach within the IVA algorithm is to model di erent speech sources with
an identical source prior, however this does not account for the unique characteristics
of each speech signal. Therefore dependency modelling for di erent speech sources
can be improved by modelling di erent speech sources with di erent source priors.
Hence, the Student's t mixture model (SMM) is introduced as a source prior for the
IVA algorithm. This new source prior can adapt according to the nature of di erent
speech signals and the parameters for the proposed SMM source prior are estimated
by deriving an e cient expectation maximization (EM) algorithm. As a result of this
study, a novel EM framework for the IVA algorithm with the SMM as a source prior is
proposed which is capable of separating the sources in an e cient manner.
The proposed algorithms are tested in various realistic reverberant room environments
with real speech signals. All the experiments and evaluation demonstrate the robustness
and enhanced separation performance of the proposed algorithms
Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis
Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise.
This work firstly investigates diesel engine air-borne acoustic signals characteristics and the benefits of joint time-frequency domain analysis. Secondly, the air-borne acoustic signals in the vicinity of injector head were recorded using three microphones around the fuel injector (120° apart from each other) and an Independent Component Analysis (ICA) based scheme was developed to decompose these acoustic signals. The fuel injection process characteristics were thus revealed in the time-frequency domain using Wigner-Ville distribution (WVD) technique. Consequently the energy levels around the injection process period between 11 and 5 degrees before the top dead center and of frequency band 9 to 15 kHz are calculated. The developed technique was validated by simulated signals and empirical measurements at different injection pressure levels from 250 to 210 bars in steps of 10 bars. The recovered energy levels in the tested conditions were found to be affected by the injector pressure settings
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 âreference-basedâ 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
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
This paper studies a fully Bayesian algorithm for endmember extraction and
abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral
image is decomposed as a linear combination of pure endmember spectra following
the linear mixing model. The estimation of the unknown endmember spectra is
conducted in a unified manner by generating the posterior distribution of
abundances and endmember parameters under a hierarchical Bayesian model. This
model assumes conjugate prior distributions for these parameters, accounts for
non-negativity and full-additivity constraints, and exploits the fact that the
endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is
proposed to overcome the complexity of evaluating the resulting posterior
distribution. This sampler generates samples distributed according to the
posterior distribution and estimates the unknown parameters using these
generated samples. The accuracy of the joint Bayesian estimator is illustrated
by simulations conducted on synthetic and real AVIRIS images
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
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