A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition

In this paper, we propose a novel family of windowing technique to compute Mel Frequency Cepstral Coefficient (MFCC) for automatic speaker recognition from speech. The proposed method is based on fundamental property of discrete time Fourier transform (DTFT) related to differentiation in frequency domain. Classical windowing scheme such as Hamming window is modified to obtain derivatives of discrete time Fourier transform coefficients. It has been mathematically shown that the slope and phase of power spectrum are inherently incorporated in newly computed cepstrum. Speaker recognition systems based on our proposed family of window functions are shown to attain substantial and consistent performance improvement over baseline single tapered Hamming window as well as recently proposed multitaper windowing technique

Discrete phase approach for nanofluids flow in pipe

Nanofluid is known as a new generation of fluid and it has been introduced almost several decades ago. But its effectiveness in practical thermal engineering applications has started to diminish with time due to the several factors such as physical instability, complex procedure for production of nanofluids and its cost, instability of suspension of nanoparticles into a base fluid, choice of thermophysical properties and reliability of nanofluids. To overcome these problems, two different phases such as a base fluid (water) and nanoparticles can be considered instead of a typical nanofluid which actually acts like a fluid-solid mixture. However, the interaction between the fluid and particles needs to be investigated to assess its performance. In the present work, Eulerian- Lagrangian discrete phase model has been used with temperature dependent thermophysical properties of the base fluid (water) and nanoparticles to study the thermal performance behaviour of Al2O3 and TiO2 nanoparticles inside a horizontal pipe within the transition to turbulent flow regimes. SST and Realizable models are considered for the modelling of transition and turbulent flow fields respectively with an enhanced near wall treatment. Results reveal that the different phases for water and nanoparticles can be used instead of a nanofluid and no thermophysical properties of nanofluid are needed to explain such behaviour. Also, it is found that the enhancement of heat transfer rate is feasible and such enhancement is fully dependent of the thermal conductivity of nanoparticles as well as nanoparticles size diameters and volume concentrations

Analysis of heat transfer and entropy generation of TiO2-water nanofluid flow in a pipe under transition

Single and multi-phase numerical simulations are carried out to investigate the heat transfer and entropy generation behaviour of transitional flow of TiO2H2O nanofluid in a circular pipe. Results reveal that the small diameter of nanoparticles has the highest heat transfer rate for χ = 6% and the TiO2-water nanofluid shows higher heat transfer rate using multi-phase model compared to that of the single phase model. Also no optimal Reynolds has been observed which could minimise the total entropy generation. New correlations are proposed to calculate the average Nusselt number using a nonlinear regression analysis with a standard deviation of error of less than 0.5%

Stochastic modeling of the grading pattern in presence of the environmental parameter

This paper deals with the stochastic modeling of grading pattern, where grades are supposed to be influenced by environmental parameter c (say), (0 ≤ c ≤ 1). The model discussed here is an extension of Wang’s model (1981). The correlation coefficient between the two consecutive states is showed to be a function of the environmental parameter ‘c’. The operating characteristics namely first passage time distributions, mean waiting time in a particular grade are obtained. Also Numerical examples are worked out to study the limiting behavior of the change