3,371 research outputs found
Joint Filtering Scheme for Nonstationary Noise Reduction
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201
Spectral analysis for nonstationary audio
A new approach for the analysis of nonstationary signals is proposed, with a
focus on audio applications. Following earlier contributions, nonstationarity
is modeled via stationarity-breaking operators acting on Gaussian stationary
random signals. The focus is on time warping and amplitude modulation, and an
approximate maximum-likelihood approach based on suitable approximations in the
wavelet transform domain is developed. This paper provides theoretical analysis
of the approximations, and introduces JEFAS, a corresponding estimation
algorithm. The latter is tested and validated on synthetic as well as real
audio signal.Comment: IEEE/ACM Transactions on Audio, Speech and Language Processing,
Institute of Electrical and Electronics Engineers, In pres
Knowledge-aided STAP in heterogeneous clutter using a hierarchical bayesian algorithm
This paper addresses the problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge, under the framework of knowledge-aided space-time adaptive processing (KA-STAP). More precisely, a Gaussian scenario is considered where the covariance matrix of the secondary data may differ from the one of interest. Additionally, some knowledge on the primary data is supposed to be available and summarized into a prior matrix. Two KA-estimation schemes are presented in a Bayesian framework whereby the minimum mean square error (MMSE) estimates are derived. The first scheme is an extension of a previous work and takes into account the non-homogeneity via an original relation. {In search of simplicity and to reduce the computational load, a second estimation scheme, less complex, is proposed and omits the fact that the environment may be heterogeneous.} Along the estimation process, not only the covariance matrix is estimated but also some parameters representing the degree of \emph{a priori} and/or the degree of heterogeneity. Performance of the two approaches are then compared using STAP synthetic data. STAP filter shapes are analyzed and also compared with a colored loading technique
Robust Reduced-Rank Adaptive Processing Based on Parallel Subgradient Projection and Krylov Subspace Techniques
In this paper, we propose a novel reduced-rank adaptive filtering algorithm
by blending the idea of the Krylov subspace methods with the set-theoretic
adaptive filtering framework. Unlike the existing Krylov-subspace-based
reduced-rank methods, the proposed algorithm tracks the optimal point in the
sense of minimizing the \sinq{true} mean square error (MSE) in the Krylov
subspace, even when the estimated statistics become erroneous (e.g., due to
sudden changes of environments). Therefore, compared with those existing
methods, the proposed algorithm is more suited to adaptive filtering
applications. The algorithm is analyzed based on a modified version of the
adaptive projected subgradient method (APSM). Numerical examples demonstrate
that the proposed algorithm enjoys better tracking performance than the
existing methods for the interference suppression problem in code-division
multiple-access (CDMA) systems as well as for simple system identification
problems.Comment: 10 figures. In IEEE Transactions on Signal Processing, 201
Femtosecond Resolution Experiments at Third-Generation Light Sources: a Concept Based on the Statistical Properties of Synchrotron Radiation
The paper describes a new concept of visible pump/X-ray probe/slow detector
experiments that could be performed at third-generation synchrotron light
sources. We propose a technique that would allow time resolution up to
femtosecond capabilities to be recovered from a long (100 ps) X-ray probe
pulse. The visible pump pulse must be as short as the desired time resolution.
The principle of operation of the proposed pump-probe scheme is essentially
based on the statistical properties of the synchrotron radiation. These
properties are well known in statistical optics as properties of completely
chaotic polarized light. Our technique utilizes the fact that, for any
synchrotron light beam there exist some characteristic time (coherence time),
which determines the time-scale of the random fluctuations. The typical
coherence time of soft X-ray synchrotron light at the exit of monochromator is
in the femtosecond range. An excited state is prepared with a pump pulse and
then projected with a probe pulse onto a final ion state. The first statistical
quantity of interest is the variance of the number of photoelectrons detected
during synchrotron radiation pulse. The statistics of concern are defined over
an ensemble of synchrotron radiation pulses. From a set of variances measured
as a function of coherence time (inversely proportional to monochromator
bandwidth) it is possible to reconstruct the femtosecond dynamical process.Comment: 54 pages, 20 figure
Codebook-based Bayesian speech enhancement for nonstationary environments
In this paper, we propose a Bayesian minimum mean squared error approach for the joint estimation of the short-term predictor parameters of speech and noise, from the noisy observation. We use trained codebooks of speech and noise linear predictive coefficients to model the a priori information required by the Bayesian scheme. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a priori information, in the proposed method they are computed online for each short-time segment, based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We develop both memoryless (using information from the current frame alone) and memory-based (using information from the current and previous frames) estimators. Estimation of functions of the short-term predictor parameters is also addressed, in particular one that leads to the minimum mean squared error estimate of the clean speech signal. Experiments indicate that the scheme proposed in this paper performs significantly better than competing method
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