Wavelet transform multi-stage identification system is presented. This paper intends to introduce high accuracy of identification the speech signal of very difficult nature that is nonstationary. Three methods are used to extract the essential speaker features based on Continuous, Discrete Wavelet Transform and Statistical Quality Evaluation Method. To have better identification rate three meas...

Continuous and Discrete Wavelet Transform (WT) are used to create text-dependent robust to noise speaker recognition system. In this paper we investigate the accuracy of identification the speaker identity in nonstationary signals. Three methods are used to extract the essential speaker features based on Continuous, Discrete Wavelet Transform and Power Spectrum Density (PSD). To have better ide...

We extend the wavelet transform to the space of periodic Boehmians and discuss some of its properties. 1. Introduction. The concept of Boehmians was introduced by J. Mikusi´nski and P. Mikusi´nski [7], and the space of Boehmians with two notions of conver-gences was well established in [8]. Many integral transforms have been extended to the context of Boehmian spaces, for example, Fourier trans...

Wavelet analysis is a new mathematical technique and in the recent years enormous interest in application of engineering has been observed. This new technique is particularly suitable for non-stationary processes as in contrast to the Fourier transform (FT). The wavelet transform (WT) allows exceptional localization, both in time and frequency domains. The application of the WT to earthquake en...

The wavelet transform is denoted by ‘WT’ gained wide-ranging approval in processing signal and in compressing image. A 2D analysis must be implemented to use WT for processing the image. Such powerful signal analysis technique can be implemented for non-stationary data using Lifting architecture for the Discrete Wavelet Transform (DWT). Since high speed implementation with low latency is a dema...

In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. A MAP Estimator is designed for this purpose which uses Rayleigh distribution for modeling the speckle noise and Laplacian distribution ...

The P seismic phase first arrival identification is a fundamental problem in seismology. The accurate identification of the P-wave first arrival is not a trivial process, especially when the seismograms present a very low signal-to noise ratio (SNR) or are contaminated with artificial transients that could produce false alarms. In this paper, a new approach based on higher-order statistics and ...

We encounter a wide range of sounds everyday which are harmonic, transient or a mixture of both. A number of models have been developed in recent years to synthesise harmonic sounds but these models do not perform effectively on transient sounds because of the sharp attack and decay part of the transient sounds. This paper presents a new technique for analysis-based synthesis of transient sound...

Synthetic aperture radar (SAR) images are inherently degraded by multiplicative speckle noise where thresholdingbased methods in the transform domain are appropriate. Being sparse, the coefficients in the transformed domain play a key role in the performance of any thresholding methods. It has been shown that the coefficients of nonsubsampled shearlet transform (NSST) are sparser than those of ...

The roller bearing characteristic frequencies contain very little energy, and are usually overwhelmed by noise and higher levels of structural vibrations. Therefore, envelope spectrum analysis is widely used to detection bearing localized fault. In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new ap...