An artificial neural network is an important model for training features of voice conversion (VC) tasks. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as Mel Cepstral Coefficients (MCC), which represent the spectrum features. However, a simple representation of fundamental frequency (F0) is not enough for NNs to deal with emotional voice VC. This is ...

Resolution enhancement(RE)methods that are independent of wavelets(interpolation methods) leads to blurring as high frequency components are lost.RE scheme based on Discrete wavelet transform(DWT) produces artifacts due to shift variant property.A complex wavelet-domain image resolution enhancement algorithm based on interpolation of the high-frequency subband images obtained by dual-tree compl...

The main goal of the present work is to study the coherent phonon in strongly confined CdSe quantum dots (QDs) under varied pump fluences. The main characteristics of coherent phonons (amplitude, frequency, phase, spectrogram) of CdSe QDs under the red-edge pump of the excitonic band [1S(e)-1S3/2(h)] are reported. We demonstrate for the first time that the amplitude of the coherent optical long...

The dynamics and diagnostics of cracked rotors have been gaining importance in recent years. The present work deals with the detection of crack in rotor system and determination of crack parameters i.e. position of crack, depth of crack using Artificial Neural Networks (ANN). The detection of crack is based on the continuous wavelet transform (CWT) plots with 1/3 and 1⁄2 critical peaks as the c...

Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Four...

The paper depicts and elaborates a new framework for the comparison of classifiers for medical image segmentation with transform and moment based features. Medical images modalities such as Ultrasound (US) bladder, Ultrasound (US) phantom, Computerized Tomography (CT) and Magnetic Resonance (MR) images are segmented using different algorithms namely, k-Nearest Neighbor (kNN), Grow and learn (GA...

Although it is generally assumed that mental stress induces muscular tension, the experimental data have, so far, been inconclusive. Likely explanations for these inconsistent findings are (a) too small subject samples in some experiments, (b) the use of only one type of stress stimulation, and (c) the lack of objective (physiological) measurements documenting the stress-inducing properties of ...

the article introduces cyclic dilation groups and finite affine groups for prime integers, and  as an application of this theory it presents a unified group theoretical approach for the  cyclic wavelet transform (cwt) of prime dimensional periodic signals.

In this paper we propose an approach to detect microcalcifications in digital mammograms using the dual-tree complex wavelet transform (DT-CWT).The approach follows four basic strategies, namely, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. Recently, the DT-CWT has shown a good performance in applications that involve image processing du...

The continuous wavelet transform (CWT) is specifically efficient in the analysis of transient and non-stationary signals. As such, it has become a powerful candidate for time-frequency analysis of cardiovascular variability. CWT has already been established as a valid tool for the analysis of single cardiovascular signals, providing additional insights into the autonomous nervous system (ANS) a...