The discrete wavelet transform has been an exciting topic of mathematical research for about 10 years now. Some of the early wavelet research had seismic applications explicitly in mind, but elds as diverse as quantum physics and voice coding have also provided insights leading to the development of the modern theory. Our purpose is to explain and illustrate the eeect of the discrete wavelet tr...

introduction: one of the major problems in the development of nuclear medicine images is the presence of noise. the noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a fourier transform environment. the main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times...

This paper describes a novel speech signal classification scheme, based on spectrograms which are subjected to wavelet transform: a procedure which yields specific information regarding time and frequency variation of the signal. Feature vectors are extracted and classified using LVQ networks. The output of the network is interpreted as a fuzzy membership coefficient. This scheme is applied to ...

Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying spea...

Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wave...

In this paper, we present an edge detection method based on wavelet transform and Hessian matrix of image at each pixel. Many methods which based on wavelet transform, use wavelet transform to approximate the gradient of image and detect edges by searching the modulus maximum of gradient vectors. In our scheme, we use wavelet transform to approximate Hessian matrix of image at each pixel, too. ...

In this letter, a robust voice activity detection (VAD) algorithm is presented. This proposed VAD algorithm makes use of the perceptual wavelet-packet transform and the Teager energy operator to compute a robust parameter called voice activity shape for VAD. The main advantage of this algorithm is that the preset threshold values or a priori knowledge of the SNR usually needed in conventional V...

Introduction: One of the major problems in the development of nuclear medicine images is the presence of noise. The noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a Fourier transform environment. The main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times...

Introduction: A major problem facing the patients with chronic liver diseases is the diagnostic procedure.  The conventional diagnostic method depends mainly on needle biopsy which is an invasive method. There  are  some  approaches  to  develop  a  reliable  noninvasive  method  of  evaluating  histological  changes  in  sonograms. The main characteristic used to distinguish between the normal...