Gain Adapted Optimum Mixture Estimation Scheme for Single Channel Speech Separation

This paper presents the proof of an Optimum mixture estimator for the single channel speech separation problem, which is a technique for separating two speech signals from a single recording of their mixture. The presented work is an attempt to solve a fundamental limitation in the current single channel speech separation techniques, in which it is assumed that the data used in the training as well as test phases of the separation model have the same energy levels. To overcome this limitation, a gain adapted Optimum mixture estimator is derived, which estimates the mixture of speech signals under the different signal-to-signal ratios (SSRs). Specifically, the speakers’ gains are incorporated as unknown parameters into the separation model, and then the estimator is derived in terms of the source distributions and SSR. It is demonstrated that the use of the Optimum mixture estimator results in the lower estimation error than the non-linear mapping (log and inverse-log operations)-based Mixture-Maximization (MixMax) or Quadratic estimators. The experimental results based on the real speech data also depict that the proposed estimator improves the mixture estimation performance significantly when compared with MixMax or Quadratic estimators with the gain adaptation.     

On the robustness of stride frequency estimation

The robustness of stride frequency estimation (location and spread) from stride period data is investigated using influence functions. Theoretical analysis reveals that stride frequency estimates by Stokes et al. and by direct calculation have unbounded influence functions and zero breakdown points, implying a lack of both local and global robustness. Comparison of estimates obtained from an ensemble of pathological gait stride time series shows that on average, differences among estimators are not statistically significant (p > or = 0.59) for long time series (hundreds of strides). Specific circumstances under which nonrobust estimates depart from robust estimates are investigated in terms of outlier influence. We recommend some heuristic rules-of-thumb for prudent selection of nonrobust stride frequency estimators for a given stride time series. The theoretical and empirical estimator comparisons suggest that in general, more research on estimator robustness in quantitative gait analysis is warranted.     

Wavelet-based estimators of scaling behavior

Various wavelet-based estimators of self-similarity or long-range dependence scaling exponent are studied extensively. These estimators mainly include the (bi)orthogonal wavelet estimators and the wavelet transform modulus maxima (WTMM) estimator. This study focuses both on short and long time-series. In the framework of fractional autoregressive integrated moving average (FARIMA) processes, we advocate the use of approximately adapted wavelet estimators. For these "ideal" processes, the scaling behavior actually extends down to the smallest scale, i.e., the sampling period of the time series, if an adapted decomposition is used. But in practical situations, there generally exists a cutoff scale below which the scaling behavior no longer holds. We test the robustness of the set of wavelet-based estimators with respect to that cutoff scale as well as to the specific density of the underlying law of the process. In all situations, the WTMM estimator is shown to be the best or among the best estimators in terms of the mean-squared error (MSE). We also compare the wavelet estimators with the detrended fluctuation analysis (DFA) estimator which was previously proved to be among the best estimators which are not wavelet-based estimators. The WTMM estimator turns out to be a very competitive estimator which can be further generalized to characterize multiscaling behavior     

Local Linear Estimators for the Bioelectromagnetic Inverse Problem

Linear estimators have been used widely in the bioelectromagnetic inverse problem, but their properties and relationships have not been fully characterized. Here, we show that the most widely used linear estimators may be characterized by a choice of norms on signal space and on source space. These norms depend, in part, on assumptions about the signal space and source space covariances. We demonstrate that two estimator classes (standardized and weight vector normalized) yield unbiased estimators of source location for simple source models (including only the noise-free case) but biased estimators of source magnitude. In the presence of instrumental (white) noise, we show that the nonadaptive standardized estimator is a biased estimator of source location, while the adaptive weight vector normalized estimator remains unbiased. A third class (distortionless) is an unbiased estimator of source magnitude but a biased estimator of source location.     

Estimation methods for the mean of the exponential distributionbased on grouped and censored data

For grouped and censored data from an exponential distribution, the method of maximum likelihood (ML) does not in general yield a closed-form estimate of the mean, and therefore, an iterative procedure must be used. Considered are three approximate estimators of the mean: two approximate ML estimators and the midpoint estimator. Their performances are compared by Monte Carlo simulation to those of the ML estimator, in terms of the mean square error and bias. The two approximate ML estimators are reasonable substitutes for the ML estimator, unless the probability of censoring and the number of inspections are small. The effect of inspection schemes on the relative performances of the three approximate methods is investigated     

带判决反馈的盲最大似然序列估计

本文提出了一种新型的带有判决反馈的减小状态最大似然序列估计ＲＳＳＤＦＰＳＰ,新算法带有两个信道估值器并且可以工作在盲环境下．使用最大似然序列估计（ＭＬＳＥ）来处理信道冲激响应的前导干扰及主径,反馈滤波器处理后尾干扰,并且用ＰｅｒＳｕｒｖｉｖｉｎｇＰｒｏｃｅｓｉｎｇ（ＰＳＰ）算法来得到ＭＬＳＥ部分的信道冲激响应,信道估值器２得到后尾干扰．计算机模拟表明,这种ＲＳＳＤＦＰＳＰ方案在减小ＭＬＳＥ的计算复杂度的同时能最大限度地得到ＭＬＳＥ的性能,是ＭＬＳＥ在计算复杂度与性能之间的较好折中．     

Monte Carlo Comparisons of Parameter Estimators of the 2-parameter Weibull Distribution

A Monte Carlo Simulation was carried out in order to compare three different estimators of the 2-parameter Weibull distribution. The estimators were the ML (maximum likelihood) estimators and two other estimator pairs suggested by Bain & Antle. The Bain-Antle estimators are better than the ML estimator for small samples (in that their bias, standard deviation, and rms error are smaller), whereas the ML estimator is superior in large samples.     

Optimal error exponents in hidden Markov models order estimation

We consider the estimation of the number of hidden states (the order) of a discrete-time finite-alphabet hidden Markov model (HMM). The estimators we investigate are related to code-based order estimators: penalized maximum-likelihood (ML) estimators and penalized versions of the mixture estimator introduced by Liu and Narayan (1994). We prove strong consistency of those estimators without assuming any a priori upper bound on the order and smaller penalties than previous works. We prove a version of Stein's lemma for HMM order estimation and derive an upper bound on underestimation exponents. Then we prove that this upper bound can be achieved by the penalized ML estimator and by the penalized mixture estimator. The proof of the latter result gets around the elusive nature of the ML in HMM by resorting to large-deviation techniques for empirical processes. Finally, we prove that for any consistent HMM order estimator, for most HMM, the overestimation exponent is .     

The use of fuzzy integrals and bispectral analysis of the electroencephalogram to predict movement under anesthesia

The objective of this study was to design and evaluate a methodology for estimating the depth of anesthesia in a canine model that integrates electroencephalogram (EEG)-derived autoregressive (AR) parameters, hemodynamic parameters, and the alveolar anesthetic concentration. Using a parameters, and the alveolar anesthetic concentration. Using a parametric approach, two separate AR models of order ten were derived for the EEG, one from the third-order cumulant sequence and the other from the autocorrelation lags of the EEG. Since the anesthetic dose versus depth of anesthesia curve is highly nonlinear, a neural network (NN) was chosen as the basic estimator and a multiple NN approach was conceived which took hemodynamic parameters, EEG derived parameters, and anesthetic concentration as input feature vectors. Since the estimation of the depth of anesthesia involves cognitive as well as statistical uncertainties, a fuzzy integral was used to integrate the individual estimates of the various networks and to arrive at the final estimate of the depth of anesthesia. Data from 11 experiments were used to train the NN's which were then tested on nine other experiments. The fuzzy integral of the individual NN estimates (when tested on 43 feature vectors from seven of the nine test experiments) classified 40 (93%) of them correctly, offering a substantial improvement over the individual NN estimates.     

Optimal blind carrier recovery for MPSK burst transmissions

The paper introduces and analyzes the asymptotic (large sample) performance of a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of carrier phase, frequency offset, and Doppler rate for burst-mode phase-shift keying transmissions. An optimal or "matched" nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged blind NLS estimators is developed. The asymptotic variance of these estimators is established in closed-form expression and shown to approach the Cramer-Rao lower bound of an unmodulated carrier at medium and high signal-to-noise ratios (SNR). Monomial nonlinear estimators that do not depend on the SNR are also introduced and shown to perform similarly to the SNR-dependent matched nonlinear estimator. Computer simulations are presented to corroborate the theoretical performance analysis.     

螺距跳变技术优化行波管电子效率的研究

行波管采用动态速度渐变（DVT）技术能获得很高的电子互作用效率。这里采用粒子模拟（PIC）技术对Ka波段某螺旋线空间行波管慢波结构进行模拟,分析了采用DVT技术的慢波结构内部各段螺距的大小和长度对行波管电子效率以及增益的影响。对慢波结构进行优化,优化后,电子效率由17．53％提高至27．27％,增益由51．17dB增加至53．09dB。     

Mobile unit velocity estimation based on the instantaneous frequency of the received signal

This paper presents a new mobile station velocity estimator based on the first moment of the instantaneous frequency (IF) of the received signal. The effects of shadowing, additive noise, and scattering distribution on the proposed velocity estimator are analyzed. We show that, unlike velocity estimators based on the envelope and quadrature components of the received signal, the proposed estimator is robust to shadowing. We also prove that the performance of the IF-based estimator is only mildly affected by the presence of additive noise. Finally, by using simulations we show that the performance of the proposed IF-based estimator is superior to that of existing velocity estimators.     

基于环路增益的负反馈放大电路分析

文献[1]根据|1+AF|来判断放大电路反馈极性和负反馈放大电路产生自激振荡的条件有不当之处,容易给学生造成误解。本文利用环路增益AF来判断放大电路反馈极性、反馈深度并讨论稳定性。首次采用环路增益的奈奎斯特图来分析反馈极性、反馈深度和稳定性。实验结果表明环路增益奈奎斯特图的成功引入,非常简单直观的解决了文献[1]中的不当之处,并给出了判断放大电路反馈的正负极性、反馈深度和稳定性的准确描述。     

The statistical performance of some instantaneous frequencyestimators

The authors examine the class of smoothed central finite difference (SCFD) instantaneous frequency (IF) estimators which are based on finite differencing of the phase of the analytic signal. These estimators are closely related to IF estimation via the (periodic) first moment, with respect to frequency of discrete time-frequency representations (TFRs) in L. Cohen's (1966) class. The authors determine the distribution of this class of estimators and establish a framework which allows the comparison of several other estimators such as the zero-crossing estimator and one based on linear regression on the signal phase. It is found that the regression IF estimator is biased and exhibits a large threshold for much of the frequency range. By replacing the linear convolution operation in the regression estimator with the appropriate convolution operation for circular data the authors obtain the parabolic SCFD (PSCFD) estimator, which is unbiased and has a frequency-independent variance, yet retains the optimal performance and simplicity of the original estimator     

Covariance shaping least-squares estimation

A new linear estimator is proposed, which we refer to as the covariance shaping least-squares (CSLS) estimator, for estimating a set of unknown deterministic parameters, x, observed through a known linear transformation H and corrupted by additive noise. The CSLS estimator is a biased estimator directed at improving the performance of the traditional least-squares (LS) estimator by choosing the estimate of x to minimize the (weighted) total error variance in the observations subject to a constraint on the covariance of the estimation error so that we control the dynamic range and spectral shape of the covariance of the estimation error. The presented CSLS estimator is shown to achieve the Cramer-Rao lower bound for biased estimators. Furthermore, analysis of the mean-squared error (MSE) of both the CSLS estimator and the LS estimator demonstrates that the covariance of the estimation error can be chosen such that there is a threshold SNR below which the CSLS estimator yields a lower MSE than the LS estimator for all values of x. As we show, some of the well-known modifications of the LS estimator can be formulated as CSLS estimators. This allows us to interpret these estimators as the estimators that minimize the total error variance in the observations, among all linear estimators with the same covariance.     

System reliability estimation using simulation combined with network reductions

This paper proposes a dynamic Monte Carlo sampling method, called the conditional minimal cut set (COMICS) algorithm, where all arcs are not simulated at each trial and all minimal cut sets need not be given in advance. The proposed algorithm repeats simulating a minimal cut set composed of the arcs which originate from the (new) source node and reducing the network on the basis of the states of simulated arcs until the s-t connectedness is confirmed. We develop the importance sampling estimator, the total hazard estimator and the hazard importance sampling estimator which are all based on the proposed algorithm, and compare the performance of these simulation estimators. It is found that these estimators can significantly reduce the variance of the raw simulation estimator and the usual importance sampling estimator.     

Estimation of hidden state variables of the Intracranial system using constrained nonlinear Kalman filters

Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment.     

Restricted structure optimal linear pseudo-state filtering fordiscrete-time systems

A new class of discrete-time optimal linear estimators is introduced that minimizes a minimum variance criterion but where the structure is prespecified to have a relatively simple form. There are three new ideas on which the results depend. The first is the use of the pseudo-state modeling approach, with the estimator represented in an observer form. The second is the use of a restricted structure gain calculation that enables the optimal estimator to be generated but where the gain transfer function is only allowed to be of a prespecified form. The third idea introduced is to borrow a very successful structure used in control systems, that is, to propose a proportional, integral, derivative structure for the observer gain calculation. The resulting estimator can be of much lower order than a Kalman or Wiener estimator, and it minimizes the estimation error variance subject to the constraint referred to above     

On Least Squares Weighted Recursive Estimation of Clock Skew and Offset in Wireless Sensor Networks

Three clock synchronization algorithms for Wireless sensor networks (WSNs) in Pairwise broadcast synchronization (PBS) mechanism are derived.They include the joint Least squares estimator (LS),joint Least squares weighted estimator (LSW) and joint Least squares weighted Recursive estimator (R-LSW).For these estimators,the corresponding algorithms are derived and described by assuming a Gaussian random delay model.Unlike PBS,these estimators can achieve the Cramer-Rao lower bound (CPLB) for both listening node and active node without knowledge of the deterministic delay.The purpose of considering R-LSW is to reduce the use of storage space with the method of estimating while observing.Simulation and analytical results verify that the estimators are efficient.     

A Carrier-Independent Non-Data-Aided Real-Time SNR Estimator for M-PSK and D-MPSK Suitable for FPGAs and ASICs

We present a channel signal-to-noise ratio (SNR) estimator for $M$-ary phase shift keying (M-PSK) and differential M-PSK. The estimator is non data aided and is shown to have the following advantages: 1) It does not require prior carrier synchronization; 2) the estimator has a compact fixed-point hardware implementation suitable for field-programmable gate arrays and application-specific integrated circuits; 3) it requires only 1 sample/symbol; 4) accurate estimates can be generated in real time; and 5) the estimator is resistant to imperfections in the automatic gain control circuit. We investigate the proposed estimator theoretically and through simulations. In particular, we investigate the required quantization necessary to achieve a good estimator performance. General formulas are developed for SNR estimation in the presence of frequency-flat slow fading, and specific results are presented for Nakagami- $m$ fading. The proposed estimator is then compared with other SNR estimators, and it is shown that the proposed method requires less hardware resources while, at the same time, providing comparable or superior performance.