基于先验信噪比估计和增益平滑的语音增强

针对语音增强算法残留“音乐噪声”的问题,分析了基于先验信噪比估计的语音增强算法,并在此基础上提出自适应先验信噪比估计与增益平滑相结合的方法.这种方法先对先验信嗓比进行估计,然后对增益函数进行平滑,减小相邻增益函数的随机跳变,弥补了传统先验信噪比估计的不足.最后对含高斯白噪声的语音信号进行处理,仿真结果表明,该算法在抑制“音乐噪声”的效果上得到一定改善,提高了语音增强的性能.     

An A Posteriori Error Estimate for the Local Discontinuous Galerkin Method Applied to Linear and Nonlinear Diffusion Problems

In this paper we present a new residual-based reliable a posteriori error estimator for the local discontinuous Galerkin approximations of linear and nonlinear diffusion problems in polygonal regions of R ². Our analysis, which applies to convex and nonconvex domains, is based on Helmholtz decompositions of the error and a suitable auxiliary polynomial function interpolating the Dirichlet datum. Several examples confirming the reliability of the estimator and providing numerical evidences for its efficiency are given. Furthermore, the associated adaptive method, which considers meshes with and without hanging nodes, is shown to be much more efficient than a uniform refinement to compute the discrete solutions. In particular, the experiments illustrate the ability of the adaptive algorithm to localize the singularities of each problem.Mathematics Subject Classifications (1991). 65N30This revised version was published online in July 2005 with corrected volume and issue numbers.     

Case Based Imprecision Estimates for Bayes Classifiers with the Bayesian Bootstrap

This article outlines a Bayesian bootstrap method for case based imprecision estimates in Bayes classification. We argue that this approach is an important complement to methods such as k-fold cross validation that are based on overall error rates. It is shown how case based imprecision estimates may be used to improve Bayes classifiers under asymmetrical loss functions. In addition, other approaches to making use of case based imprecision estimates are discussed and illustrated on two real world data sets. Contrary to the common assumption, Bayesian bootstrap simulations indicate that the uncertainty associated with the output of a Bayes classifier is often far from normally distributed.     

A posteriori error estimates of two-grid finite volume element methods for nonlinear elliptic problems

In this paper, we study the a posteriori error estimates of two-grid finite volume element method for second-order nonlinear elliptic equations. We derive the residual-based a posteriori error estimator and prove the computable upper and lower bounds on the error in $H^1$-norm. The a posteriori error estimator can be used to assess the accuracy of the two-grid finite volume element solutions in practical applications. Numerical examples are provided to illustrate the performance of the proposed estimator.     

A splitting-based finite element method for the Biot poroelasticity system

In this work, we propose a finite element method for solving the linear poroelasticity equations. Both displacement and pressure are approximated by continuous piecewise polynomials. The proposed method is sequential, leading to decoupled smaller linear systems compared to the systems resulting from a fully implicit finite element approach. A priori error estimates are derived. Numerical results validate the theoretical convergence rates.     

Orthogonal spline collocation method for the fourth-order diffusion system

The fourth-order diffusion systems depict the wave and photon propagation in intense laser beams and play an important role in the phase separation in binary mixture. In this paper, by using orthogonal spline collocation (OSC) method in spatial direction and classical L1 approximation in temporal direction, a fully discrete scheme is established for a class of fourth-order fractional reaction–diffusion equations. For the original unknown $u$ and auxiliary variable $v=\Delta u$, the full-discrete unconditional stabilities based on a priori analysis are derived by virtue of properties of OSC. Moreover, the convergence rates in $L^2$-norm for unknown $u$ are strictly investigated. At the same time, the optimal error estimates in $H^1$-norm for unknown $u$ and in $L^2$-norm for variable $v$, are also derived, respectively. For further verifying the theoretical analysis, some numerical examples are provided.     

多维非线性sobolev型发展方程(组)的初边值问题

本文主要研究了如下的初边值问题其中Ω∈R~n是一有界开集,其边界Ω充分光滑。进一步证明了这个问题整体解的存在唯一及t→ ∞时解的渐近性质。     

基于自动相关判别先验的叠前同时反演方法研究

基于叠前地震数据振幅随入射角变化特征的叠前反演方法是获得储层弹性参数估计的重要手段。常规贝叶斯叠前反演假设弹性参数反射系数满足某一特定分布,利用该分布作为先验信息约束反演,且分布一经确定不再随不同地震道变化,但在弹性参数反射系数的分布横向变化较大或不满足既定分布时会产生误差。为此,通过引入自动相关判别先验,提出了一种贝叶斯叠前同时反演方法,将弹性参数反射系数的先验信息融合到反演中,该先验信息不再对弹性参数反射系数的总体分布作出假设,且随着地震数据的变化自适应改变,使弹性参数反射系数的反演结果具有更好的准确性、更符合横向变化的地质特征,并且更好地反映岩性边界;在构建反演目标函数时,加入了趋势约束算子,提高了反演的稳定性;采用同时反演策略,避免了基于弹性阻抗的叠前反演过程中存在转换误差。单道弹性参数模型和经典推覆体2D模型测试结果证实了方法的可行性和正确性,实际地震数据测试结果证实了方法在提高分辨率和反映地质体特征及边界上的优势。