On Parameter Estimation Using Nonparametric Noise Models

Fitting multidimensional parametric models in frequency domain using nonparametric noise models is considered in this paper. A nonparametric estimate of the noise statistics is obtained from a finite number of independent data sets. The estimated noise model is then substituted for the the true noise covariance matrix in the maximum likelihood loss function to obtain suboptimal parameter estimates. The goal here is to present an analysis of the resulting estimates. Sufficient conditions for consistency are derived, and an asymptotic accuracy analysis is carried out. The first- and second-order statistics of the cost function at the global minimum point are also explored, which can be used for model validation. The analytical findings are validated using numerical simulation results.     

基于噪声水平估计的图像盲去噪

三维块匹配(BM3D)去噪是当前去噪性能最好的算法之一。但由于时间复杂度较高,而且需要输入精确的图像噪声水平参数,极大地限制该算法的广泛应用。因此,文中首先采用基于网格的块匹配策略,提出快速三维块匹配(FBM3D)算法。然后提出基于迭代的盲图像噪声水平估计算法,由SVM学习算法确定迭代的初始值,再由图像质量判定迭代是否终止。测试实验表明,与原始的BM3D算法相比,该算法在计算效率、视觉感知效果和定量评测方面均有明显改善。     

Distributed and Recursive Parameter Estimation in Parametrized Linear State-Space Models

We consider a network of sensors deployed to sense a spatio-temporal field and infer parameters of interest about the field. We are interested in the case where each sensor's observation sequence is modeled as a state-space process that is perturbed by random noise, and the models across sensors are parametrized by the same parameter vector. The sensors collaborate to estimate this parameter from their measurements, and to this end we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms.      

Input Disturbance Estimation For Linear Systems in The Bounded Noise Context

A method is presented for constructing input disturbance estimates for a linear system using noisy observations. The input disturbance of the dynamic system and the observation noise are assumed to be unknown but bounded. In addition, the structural characteristics of the input disturbance are given in the form of the maximum possible change of its magnitude per sampling time. The input disturbance represents a wide category of system uncertainties. A recursive procedure for obtaining disturbance set estimates is derived. The procedure is easy to implement and is a competitive technique comparative to the classical estimation schemes. Simulation results demonstrate the performance of the given techniques.     

空间非平稳噪声环境下非均匀线阵的DOA估计

针对在空间非平稳噪声环境下传统的MUSIC算法会失效的问题,提出了一种新的波达方向（DOA）估计方法。该方法利用转换矩阵法,通过对阵列数据相关矩阵进行预处理,从而克服噪声对方位估计的影响,并且该算法适用于非均匀线阵,而非均匀线阵在阵元数目一定的情况下,通过合理设置阵元间距。其方位分辨率较之均匀线阵有较大的提高。计算机仿真结果表明,在非平稳噪声环境下利用该算法非均匀线阵的分辨能力要明显高于均匀线阵。     

运动模糊图像的噪声功率的精确估计

由运动模糊图像复原出原图像关键问题是获取点扩展函数和噪声信息,其中噪声通常假设为高斯白噪声。针对匀速直线运动模糊图像,提出一种差分噪声功率估计方法,该方法对模糊图像的差分图进行叠加,放大噪声,并保持每列的噪声互不相关,叠加后的图像的方差与原始噪声方差存在一个数量关系,通过该数量关系来估计出原始噪声方差。实验证明该方法能够相当准确地估计出噪声功率。     

General Cost Models for Evaluating Dimensionality Reduction in High-Dimensional Spaces

Similarity search usually encounters a serious problem in the high-dimensional space, known as the “curse of dimensionality.” In order to speed up the retrieval efficiency, most previous approaches reduce the dimensionality of the entire data set to a fixed lower value before building indexes (referred to as global dimensionality reduction (GDR)). More recent works focus on locally reducing the dimensionality of data to different values (called the local dimensionality reduction (LDR)). In addition, random projection is proposed as an approximate dimensionality reduction (ADR) technique to answer the approximate similarity search instead of the exact one. However, so far little work has formally evaluated the effectiveness and efficiency of GDR, LDR, and ADR for the range query. Motivated by this, in this paper, we propose general cost models for evaluating the query performance over the reduced data sets by GDR, LDR, and ADR, in light of which we introduce a novel (A)LDR method, Partitioning based on RANdomized Search (PRANS). It can achieve high retrieval efficiency with the guarantee of optimality given by the formal models. Finally, a {rm B}^{+}-tree index is constructed over the reduced partitions for fast similarity search. Extensive experiments validate the correctness of our cost models on both real and synthetic data sets and demonstrate the efficiency and effectiveness of the proposed PRANS method.     

Noise Condition-Dependent Training Based on Noise Classification and SNR Estimation

Condition-dependent training strategy divides a training database into a number of clusters, each corresponding to a noise condition and subsequently trains a hidden Markov model (HMM) set for each cluster. This paper investigates and compares a number of condition-dependent training strategies in order to achieve a better understanding of the effects on automatic speech recogntion (ASR) performance as caused by a splitting of the training databases. Also, the relationship between mismatches in signal-to-noise ratio (SNR) is analyzed. The results show that a splitting of the training material in terms of both noise type and SNR value is advantageous compared to previously used methods, and that training of only a limited number of HMM sets is sufficient for each noise type for robustly handling of SNR mismatches. This leads to the introduction of an SNR and noise classification-based training strategy (SNT-SNC). Better ASR performance is obtained on test material containing data from known noise types as compared to either multicondition training or noise-type dependent training strategies. The computational complexity of the SNT-SNC framework is kept low by choosing only one HMM set for recognition. The HMM set is chosen on the basis of results from noise classification and SNR value estimations. However, compared to other strategies, the SNT-SNC framework shows lower performance for unknown noise types. This problem is partly overcome by introducing a number of model and feature domain techniques. Experiments using both artificially corrupted and real-world noisy speech databases are conducted and demonstrate the effectiveness of these methods.     

Solving Linear Rational Expectations Models

We describe methods for solving general linear rational expectations models in continuous or discrete timing with or without exogenous variables. The methods are based on matrix eigenvalue decompositions.     

对称二值噪声下线性系统的随机共振

对于受到外部周期力、乘性和加性的二值噪声共同作用下的线性系统,通过运用Shapiro-Loginov公式,计算出系统一阶矩和信噪比的表达式。信噪比作为加性噪声强度D2的函数,数值结果表明在负相关区域-1≤λ〈0条件下有随机共振现象出现,而在非负相关区域0λ1却未出现;作为乘性噪声强度D1的函数时,仍在负相关区域-1≤λ〈0有共振现象发生,峰值高度随D2的增大而增大,位置右移;输入信号频率ω和外部力振幅α作为信噪比的变量时,随机共振现象也被发现,峰值的位置却不随着λ、α的变化而变化。     

强脉冲噪声环境下的多径衰信道估计

提出一种在强干扰脉冲噪声存在下对无线多径信道进行估计的算法.在无线通信系统中,衰落信道可以采用自回归(AR)模型建模,通过RLS算法和自适应Kalman滤波器分别对AR模型的参数进行估计,但是,这两种算法对噪声干扰非常敏感.为了加快RLS算法的收敛性,并有效抑制大脉冲干扰的影响,在算法的改进中引入了抑制因子,用于对脉冲干扰幅度的抑制.仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估参数的收敛速度.     

Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation

This paper considers estimation of the noise spectral variance from speech signals contaminated by highly nonstationary noise sources. The method can accurately track fast changes in noise power level (up to about 10 dB/s). In each time frame, for each frequency bin, the noise variance estimate is updated recursively with the minimum mean-square error (mmse) estimate of the current noise power. A time- and frequency-dependent smoothing parameter is used, which is varied according to an estimate of speech presence probability. In this way, the amount of speech power leaking into the noise estimates is kept low. For the estimation of the noise power, a spectral gain function is used, which is found by an iterative data-driven training method. The proposed noise tracking method is tested on various stationary and nonstationary noise sources, for a wide range of signal-to-noise ratios, and compared with two state-of-the-art methods. When used in a speech enhancement system, improvements in segmental signal-to-noise ratio of more than 1 dB can be obtained for the most nonstationary noise sources at high noise levels.     

Minimax Estimation in Singular Uncertain Stochastic Models

Minimax parametric identification of a multidimensional uncertain stochastic linear model under incomplete a priori information on the first two moments of the characteristics of the parameters of the model is investigated. The minimax problem is reduced through regularization of the initial mean-square criterion to a dual problem without any additional assumptions on the nondegeneracy of matrices belonging to the uncertainty set. Results are illustrated by concrete examples of singular models.     

Speech Recognition Using Linear Dynamic Models

The majority of automatic speech recognition systems rely on hidden Markov models, in which Gaussian mixtures model the output distributions associated with sub-phone states. This approach, whilst successful, models consecutive feature vectors (augmented to include derivative information) as statistically independent. Furthermore, spatial correlations present in speech parameters are frequently ignored through the use of diagonal covariance matrices. This paper continues the work of Digalakis and others who proposed instead a first-order linear state-space model which has the capacity to model underlying dynamics, and furthermore give a model of spatial correlations. This paper examines the assumptions made in applying such a model and shows that the addition of a hidden dynamic state leads to increases in accuracy over otherwise equivalent static models. We also propose a time-asynchronous decoding strategy suited to recognition with segment models. We describe implementation of decoding for linear dynamic models and present TIMIT phone recognition results     

Nonparametric Frontier Estimation by Linear Programming

A new method for estimating the frontier of a set of points (or a support, in other words) is proposed. The estimates are defined as kernel functions covering all the points and whose associated support is of smallest surface. They are written as linear combinations of kernel functions applied to the points of the sample. The weights of the linear combination are then computed by solving a linear programming problem. In the general case, the solution of the optimization problem is sparse, that is, only a few coefficients are non zero. The corresponding points play the role of support vectors in the statistical learning theory. The L ₁-norm for the error of estimation is shown to be almost surely converging to zero, and the rate of convergence is provided.     

基于时间序列分析的水位噪声信号仿真

汽包水位的控制关系到整个联合循环电厂的运行效率和运行安全.由于水位信号中往往包含着噪声信号,因此水位噪声信号对水位控制系统的影响在进行控制系统设计和仿真时都必须加以考虑.然而已有的大多数余热锅炉仿真模型都没有考虑汽包水位噪声的影响.该文采用时间序列分析的方法,对现场采集的汽包水位噪声信号进行了分析,采用Burg方法建立了相应的水位噪声信号AR模型;该模型从功率谱上对现场数据进行了匹配模拟,使用该模型作为噪声模块在时域上成功地重建了汽包水位噪声信号,为开发余热锅炉汽包水位先进控制策略奠定了基础.     

任意均值复杂噪声下三次非线性耦合谐波分析

针对零均值乘性噪声和加性噪声共存,并且乘性噪声之间独立、乘性噪声和加性噪声之间也独立的噪声背景下谐波的三次非线性耦合问题,提出了一种特殊定义的四阶时间平均多矩谱方法.此方法能够有效地估计出观测信号中参与耦合的谐波频率,文中给出了详细的理论分析和证明.由于该方法也同样适合于非零均值噪声下的谐波耦合问题,因此不再需要对噪声的均值、颜色和分布作任何限制,从而对噪声的统计特性及分布的限制降到了最低,仿真结果表明了该方法的有效性.     

一种对称噪声环境下DOA估计的新方法

研究水下目标优化估算精度问题。在满足对称分布的海洋环境噪声中进行波达方向(DOA)估计的方法对于解决水下目标感知问题有重要意义。由于水下存在的海洋噪声等识别目标难度大,提出了一种重构数据协方差矩阵实部的DOA估计。通过消除数据协方差矩阵实部来降低对称噪声的影响,然后对协方差矩阵实部进行重新构造,恢复损失的目标信息,实现精确DOA估计。与传统方法的比较,方法能够有效降低对称噪声影响,避免双边谱的出现,提高了估计精度。仿真结果表明,方法性能优良,对于海洋环境中DOA估计的研究提供了参考。