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This paper compares wavelet and short time Fourier transform based techniques for single channel speechsignal noise reduction. Despite success of wavelet denoising of images, it has not yet been widel...
The purpose of this note is to present an extension and an alternative proof to Theorem 1.3 from G. Battle (Appl. Comput. Harmonic Anal. 4 (1997) 119–146). This extension applies to wavelet Bessel set...
In this paper we present two applications of a Stability Theorem of Hilbert frames to nonharmonic Fourier series and wavelet Riesz basis. The first result is an enhancement of the Paley-Wiener type co...
With ideal spatial adaptation, an oracle furnishes information about how best to adapt a spatially variable estimator, whether piecewise constant, piecewise polynomial, variable knot spline, or vari...
Pinsker(1980) gave a precise asymptotic evaluation of the minimax mean squared error of estimation of a signal in Gaussian noise when the signal is known a priori to lie in a compact ellipsoid in Hi...
This paper explores a class of empirical Bayes methods for levedependent threshold selection in wavelet shrinkage. The prior considered for each wavelet coefficient is a mixture of an atom of p...
Deconvolution problems are naturally represented in the Fourier domain, whereas thresholding in wavelet bases is known to have broad adaptivity properties. We study a method which combines both fast...
Johnstone and Silverman (1997) described a level-dependent thresholding method for extracting signals from correlated noise. The thresholds were chosen to minimize a data based unbiased risk criteri...
Donoho and Johnstone (1998) studied a setting where data were obtained in the continuum white noise model and showed that scalar nonlinearities applied to wavelet coefficients gave estimators w...
Wavelets have motivated development of a host of new ideas in nonparametric regression smoothing. Here we apply the tool of exact risk analysis, to understand the small sample behavior of wavelet es...
We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets we develop a nonlinear method which works in the wavelet domain by simple ...
We discuss a method for curve estimation based on n noisy data; one translates the empirical wavelet coecients towards the origin by an amount p 2 log(n)  =p n. The method is nearly minimax for...
Density estimation is a commonly used test case for non-parametric estimation methods. We explore the asymptotic properties of estimators based on thresholding of empirical wavelet coecients. Minim...
Considerable e ort has been directed recently to develop asymptotically minimax methods in problems of recovering in nite-dimensional ob jects (curves, densities, spectral densities, images) from nois...
A model for the definition of electrical Power is presented, which retrieves the concepts of homomorphism from the geometrical tensor approximation at the wavelet approach. Their definition here is ne...

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