In audio applications it is often necessary to process the signal in “real time”. The method of segmented wavelet transform (SegWT) makes it possible to compute the discrete-time wavelet transform of a signal segment-by-segment, not using the classical “windowing”. This means that the method could be utilized for wavelettype processing of an audio signal in real time, or alternatively in case w...

We present a construction of a wavelet-type orthonormal basis for the space of radial L-functions in R via the concept of a radial multiresolution analysis. The elements of the basis are obtained from a single radial wavelet by usual dilations and generalized translations. Hereby the generalized translation reveals the group convolution of radial functions in R. We provide a simple way to const...

This work compares a few attempts based on Wavelet and Neural networks, for extracting the Jominy hardness pro les of steels directly from the chemical composition. That is essentially a black-box modeling problem: Wavelet and Neural networks seem powerful, especially when compared with classical methods commonly found in literature. In particular, the paper proposes a multi-network architectur...

We present two strategies for detecting patterns and clusters in high-dimensional timedependent functional data. The use on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant local time and scale features. The multiresolution aspect of the wavelet transform provides a time-scale decomposition of the signals allowing to visualize and to cluster ...

In this paper we study a generalization of the Donoho-Johnstone denoising model for the case of the translation invariant wavelet transform. Instead of soft-thresholding coeecients of the classical orthogonal discrete wavelet transform, we study soft-thresholding of the co-eecients of the translation invariant discrete wavelet transform. This latter transform is not an orthogonal transformation...

An analytic wavelet transform, based on Hilbert wavelet pairs, is applied to bivariate time-varying spectral estimation for neurophysiological time series. Under the assumption of an underlying block stationary process, both single-trial and ensemble studies are amenable to this method. A bootstrap procedure, which samples with replacement blocks centered around the events of interest, is propo...

Optimizing threshold value of wavelet coefficient is an important task in speckle noise reduction in the wavelet domain. Without proper selection of threshold value image information may be lost, which is unwanted. In this paper we proposed optimum threshold parameter using Fisher Discriminant Analysis (FDA) for determining the optimum threshold value of wavelet coefficient for the best speckle...

SUMMARY Wavelet estimation plays an important role in many seismic processes like impedance inversion, amplitude versus offset (AVO) and full waveform inversion (FWI). Statistical methods of wavelet estimation away from well control are a desirable tool to support seismic signal processing. One of these methods based on Homomorphic analysis has long intrigued as a potentially elegant solution t...