Reduction of residual noise based on eigencomponent filtering for speech enhancement

In this paper, residual noise of corrupted speech observations is further restrained based on eigencomponent (an eigenvalue and its corresponding eigenvector) filtering. Three relevant algorithms are proposed to obtain the core eigencomponents that deeply affect enhancement quality of speech fragments by joint diagonalization of clean speech and noise covariance matrix. In addition, the generalized inverse matrix transform is introduced to the recovery of enhanced speech signal for the issue of matrix irreversibility after eigencomponents are filtered. Experiment results show that the proposed methods work better than many other methods under various conditions on both noise reduction and speech distortion.     

Design of RLS Wiener FIR filter using covariance information in linear discrete-time stochastic systems

This paper addresses a new design method of recursive least-squares (RLS) finite impulse response (FIR) filter, using the covariance information of the signal and observation noise, and RLS Wiener FIR filter in linear discrete-time stochastic systems. The signal is observed with additive white noise. The signal is assumed to be independent of the white observation noise. The RLS Wiener FIR filter uses the following information: (1) The observation matrix for the signal, (2) the system matrix for the state vector, (3) the variance of the state vector.     

面向无线传感器网络节点定位的自适应卡尔曼滤波算法收敛条件分析

分析了新息序列是有色噪声时白适应卡尔曼滤波算法(Adaptive Kalman Filter,AKF)的滤波效果,在范数意义下,证明了k时刻AKF算法中估计误差协方差矩阵和k时刻最优KF算法中估计误差协方差矩阵间距离与新息序 列相关性成正比.利用上述结论,证明了所有AKF算法中估计误差协方差矩阵必逐渐远离1时刻最优KF算法中估计误差协方差矩阵.总结上述结论,发现AKF算法收敛条件可描述成以下几个等价命题:1)AKF算法中估计误差协方差矩阵与1时刻最优KF算法中估计误差协方差矩阵差有极限;2)k时刻AKF算法中估计误差协方差矩阵和k时刻最优KF算法中估计误差方差矩阵间距离极限是0;3)AKF算法渐进收敛于k时刻最优KF算法;4)AKF算法中新息序列渐进收敛于白噪声序列;5)k时刻AKF算法中滤波增益矩阵与k时刻最优KF算法中滤波增益矩阵间距离极限是0.上述理论为最终解决复杂环境下无线传感器网络节点定位问题奠定了基础.     

Adaptive interference removal based on concentration of the STFT

We propose a new method for adaptively removing noise and interference from a signal. In this method unwanted components are removed from the short time Fourier transform (STFT) surface, and the clean signal is estimated by integrating the modified STFT with respect to frequency. Isolation of the signal and interference components is facilitated by a concentration process based on the phase of the STFT differentiated with respect to time. The concentrated STFT is a linear representation, free of cross terms and having the property that signal and interference components are easily recognized because their distributions are more concentrated in frequency. Interference removal may be accomplished by removing unwanted components from the concentrated STFT, and the clean signal may be estimated by integration of the modified concentrated STFT. We demonstrate the advantages of the proposed method over conventional methods.     

脉冲噪声环境下基于卡尔曼滤波的语音增强

语音增强的目标在于从含噪信号中提取纯净语音,纯净语音在某些环境下会被脉冲噪声所污染,但脉冲噪声的时域分布特征却给语音增强带来困难,使传统方法在脉冲噪声环境下难以取得满意效果。为在平稳脉冲噪声环境下进行语音增强,提出了一种新方法。该方法通过计算确定脉冲噪声样本的能量与含噪信号样本的能量之比最大的频段,利用该频段能量分布情况逐帧判别语音信号是否被脉冲噪声所污染。进一步地,该方法只在被脉冲噪声污染的帧应用卡尔曼滤波算法去噪,并改进了传统算法执行时的自回归(AR)模型参数估计过程。实验中,采用白色脉冲噪声以及有色脉冲噪声污染语音信号,并对低输入信噪比的信号进行语音增强,结果表明所提出的算法能显著地改善信噪比和抑制脉冲噪声。     

Switching Linear Dynamical Systems for Noise Robust Speech Recognition

Real world applications such as hands-free dialling in cars may have to deal with potentially very noisy environments. Existing state-of-the-art solutions to this problem use feature-based HMMs, with a preprocessing stage to clean the noisy signal. However, the effect that raw signal noise has on the induced HMM features is poorly understood, and limits the performance of the HMM system. An alternative to feature-based HMMs is to model the raw signal, which has the potential advantage that including an explicit noise model is straightforward. Here we jointly model the dynamics of both the raw speech signal and the noise, using a switching linear dynamical system (SLDS). The new model was tested on isolated digit utterances corrupted by Gaussian noise. Contrary to the autoregressive HMM and its derivatives, which provides a model of uncorrupted raw speech, the SLDS is comparatively noise robust and also significantly outperforms a state-of-the-art feature-based HMM. The computational complexity of the SLDS scales exponentially with the length of the time series. To counter this we use expectation correction which provides a stable and accurate linear-time approximation for this important class of models, aiding their further application in acoustic modeling.     

Spatial smoothing based methods for direction-of-arrival estimation of coherent signals in nonuniform noise

Spatial smoothing techniques have been widely used to estimate the directions-of-arrival (DOAs) of coherent signals. However, in general these techniques are derived under the condition of uniform white noise and, therefore, their performance may be significantly deteriorated when nonuniform noise occurs. This motivates us to develop new methods for DOA estimation of coherent signals in nonuniform noise in this paper. In our methods, the noise covariance matrix is first directly or iteratively calculated from the array covariance matrix. Then, the noise component in the array covariance matrix is eliminated to achieve a noise-free array covariance matrix. By mitigating the effect of noise nonuniformity, conventional spatial smoothing techniques developed for uniform white noise can thus be employed to reconstruct a full-rank signal covariance matrix, which enables us to apply the subspace-based DOA estimation methods effectively. Simulation results demonstrate the effectiveness of the proposed methods.     

Time-Limited Time-Invariant Wiener Filtering

For the estimation of a signal observed with additive white noise, it is shown that the optimum linear least-squares filter constrained to have its impulse response time-limited to the interval [0,T] satisfies a truncated version of the Wiener-Hopf equation. To solve this equation the covariance for the observed process need only be known for time lags less than T. There is a unique extension of the covariance for lags greater than T, for which the time-limited filter is the optimum Wiener filter; furthermore this same extension is that extension of the covariance for which the optimum Wiener filter gives maximum mean square error, i.e., given limited covariance information we have found the “worst possible” extension of the known information. Parallels are drawn with discrete-time maximum-entropy spectral analysis.     

Detection thresholds for tracking in clutter--A connection between estimation and signal processing

In the Kalman-Bucy filter and other trackers, the dependence of tracking performance upon the quality of the measurement data is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the values of the measurement inputs. The measurement noise and process noise covariances determine, via the Riccati equation, the state estimation error covariance. When the origin of the measurements is also uncertain, one has the widely studied problem of data association (or data correlation), and tracking performance depends critically on signal processing parameters, primarily the probabilities of detection and false alarm. In this paper we derive a modified Riccati equation that quantifies (approximately) the dependence of the state error covariance on these parameters. We also show how to use a receiver operating characteristic (ROC) curve in conjunction with the above relationship to determine the detection threshold in the signal processing system that provides measurements to the tracker so as to minimize tracking errors. The approach presented in this paper provides a feedback mechanism from the information processing (tracking) subsystem to the signal processing subsystem so as to optimize the overall performance in clutter.     

Echo threshold between passive and electro-acoustic transmission paths in digital hearing protection devices

Electronic hearing protection devices are increasingly used in noisy environments. Theses devices feature a miniaturized external microphone and internal loudspeaker in addition to an analog or digital electronic circuit. They can transmit useful audio signals such as speech and warning signals to the protected ear and can reduce the sound pressure level using dynamic range compression. In the case of a digital electronic circuit, the transmission of audio signals may be noticeably delayed because of the latency introduced by the digital signal processor and by the analog-to-digital and digital-to-analog converters. These delayed audio signals will hence interfere with the audio signals perceived naturally through the passive acoustical path of the device. The proposed study presents an original procedure to evaluate, for two representative passive earplugs, the shortest delay at which human listeners start to perceive two sounds composed of the signal transmitted through the electronic circuit and the passively transmitted signal. This shortest delay is called the echo threshold and represents the delay between the time of perception of one fused sound from two separate sounds. In this study, a transient signal, a clean speech signal, a speech signal corrupted by factory noise, and a speech signal corrupted by babble noise are used to determine the echo thresholds of the two earplugs. Twenty untrained listeners participated in this study, and were asked to determine the echo thresholds using a test software in which attenuated signals are delayed from the original signals in real-time. The findings show that when using hearing devices, the echo threshold depends on four parameters: (a) the attenuation function of the device, (b) the duration of the signal, (c) the level of the background noise and (d) the type of background noise. Defined here as the shortest time delay at which at least 20% of the participants noticed an echo, the echo threshold was found to be 8 ms for a bell signal, 16 ms for clean speech and 22 ms for speech corrupted by babble noise when using a shallow earplug fit. When using a deep fit, the echo threshold was found to be 18 ms for a bell signal and 26 ms for clean speech and 68 ms for speech in factory. No echo threshold could be clearly determined for the speech signal in babble noise with a deep earplug fit.     

基于ESPRIT的噪声抑制频率估计算法

在中低信噪比时,协方差矩阵受噪声影响较大导致ESPRIT算法性能降低,使其与克拉美罗下限(CRLB)有一定距离。针对该问题,提出一种基于ESPRIT的噪声抑制频率估计算法,利用信号频域内若干子带的谱线估计协方差矩阵,通过该矩阵的特征向量张成信号子空间,估计信号各分量的频率。实验结果表明,该算法能用于多个频率分量的信号分析,归一化频率估计的范围为 ,且性能接近于CRLB下限。     

Design of recursive fixed-point smoother using covariance information in linear discrete-time systems

A recursive fixed-point smoother and filter, for linear discrete-time systems, are designed using the observed value and the covariance information of signal and observation noise for white gaussian and white gaussian plus coloured observation noise.     

Design of input signals for parameter estimation

The optimum energy-constrained and time-constrained input signal is obtained for estimating the parameters of a system. The output is corrupted by nonstationary, nonwhite additive observation noise, and the observation time is finite. The reproducing kernel Hilbert space formulation is used to obtain the parameter estimates and the error covariance matrix in terms of the input. The performance index, assumed to be a function of the error covariance matrix, is minimized by a variational procedure. A necessary condition for optimality is that the input satisfy a nonlinear Fredholm equation. An example estimates the gain of a single time constant system where the observation noise has an exponential autocorrelation function. For broadband noise, the optimum input is a portion of a sinusoid. For a noise bandwidth narrower than the system bandwidth, the optimum input switches sign as rapidly as possible, but near-optimum performance can be obtained with a relatively high frequency sinusoidal input.     

基于加权特征投影的信源数估计方法

考虑到色噪声或低快条件下噪声特征值发散,导致基于特征分解的信源数估计方法得到的信号判据值和噪声判据值区分不明显,提出了一种基于加权特征投影的信源数估计方法;首先,为了使该方法可适用于低信噪比条件,对阵列接收数据的协方差矩阵进行降噪处理,并利用降噪后协方差矩阵所有特征值和特征向量构造了一个用来区分信号和噪声的加权空间矩阵;然后,将降噪后的协方差矩阵在该加权空间矩阵上投影,从而增大了信号判据值与噪声判据值的差异;最后,结合幂函数的缩放性构建了判决函数,进而实现信源数估计;通过理论分析和实验验证,该方法不仅适用于白噪声和色噪声条件,而且在低快拍和低信噪比条件下优势明显,在快拍数为10,信噪比分别为0 dB的白噪声和6 dB的色噪声条件下,该方法的成功检测概率均达到90％以上,同时该算法在信源数较多时效果鲁棒.     

一种改进的有色噪声背景下信号的检测算法

摘要:对于自回归型(AR)的广义平稳(WSS)高斯有色噪声,以改进的协方差法得到了该噪声未知参数基于AR模型的估计,该估计优于现在广泛使用的周期图方法.然后利用渐进似然函数,建立了广义似然比(GLRT)检测器,该检测器的性能依赖于噪声的未知参数估计的精度,并且得到了检测概率和虚警概率的渐进表达式.通过对含有WSS高斯有色噪声的信号在不同信噪比下的计算机仿真实验分析,结果验证了提出方法的有效性,而且检测性能明显优于原算法.     

噪声环境下基于特征信息融合的说话人识别

针对在干净的语音环境下说话人识别率很高,但噪声环境下说话人识别率急剧下降的问题,提出了一种在噪声环境下,利用信噪比权重对说话人的特征信息MFCC系数和基音周期进行非线性融合,同时对MFCC特征参数进行基于帧信噪比权重得分,并同传统的高斯混合模型算法和基于FO-MFCC联合分布的特征融合方法,在噪声环境下分别进行了说话人识别的性能比较,同时对提出的融合算法进行了仿真实现.实验结果表明:在噪声的环境下方法相比上述传统说话人识别方法,性能有了明显的提高,在干净的语音环境下性能相当.     

Time domain signal enhancement based on an optimized singular vector denoising algorithm

This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix leads to considerable enhancement of information embedded in the matrix. The noise-reduced singular vectors from the signal subspace are utilized to reconstruct the data matrix. This matrix is finally used to obtain the time-series signal. The results of applying the proposed method to different synthetic noisy signals indicate a better efficiency in noise reduction compared to the other time series methods.     

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Wireless sensor networks are vulnerable to false data injection attacks, which may mislead the state estimation. To solve this problem, this paper presents a chi-square test-based adaptive secure state estimation (CTASSE) algorithm for state estimation and attack detection. Taking advantage of Kalman filters, attack signal together with process noise or measurement noise are described as total white Gaussian noise with uncertain covariance matrix. The chi-square test method is used in the adaptation of the total noise covariance and attack detection. Then, a standard adaptive unscented Kalman filter (UKF) is used for the state estimation. Finally, simulation results show that the proposed CTASSE algorithm performs better than other UKFs in state estimation and is also effective in real-time attack detection.     

奇异谱分析在土舱压力数据序列处理中的应用

根据土压平衡盾构机施工过程中土舱压力监测数据,利用奇异谱分析方法对土舱压力时间序列进行奇异值分解和分组重构处理,并在此基础上利用线性递归关系对其进行数值预测。结果表明奇异谱分析方法能够有效分离出土舱压力时间序列中的主要影响因素,尤其是在微弱周期性成分提取方面效果显著,并能够明显降低土舱压力信号中的噪声干扰作用,提高土舱压力预测的精度和稳定性。将奇异谱分析方法应用于土舱压力序列分析将有助于实现土舱压力的预测性控制。