基于DCT与神经网络的语音增强方法

本文提出新的语音增强方法。这种新方法先将带噪信号进行离散余弦变换,得到带噪语音信号的DCT系数,并提取这些DCT系数的参数作为神经网络的输入来估计阈值,从而构造阈值函数对变换后的系数进行处理,再进行离散余弦反变换最终达到去噪的目的。本文实验中的噪声采用加性高斯白噪声,仿真实验结果表明新方法具有良好的语音增强效果,特别是在低信噪比情况下,效果更好。     

基于高阶累积量Burg自适应算法的间谐波估计

Burg算法适合处理间谐波信号,但是在有噪声的环境下,会产生错误的谱估计结果。采用一种基于高阶累积量的Burg自适应算法估计间谐波信号,该方法不受高斯噪声的影响。首先,利用高阶累积量对高斯噪声不敏感的特性,改进Burg算法的预测误差标准。其次,为了降低引入高阶累积量而增加的计算量,反射系数的求取采用递推形式。最后,利用谐波信号的4阶累积量对角切片求取各成分的幅值。仿真结果表明,所提出的算法明显改进了Burg算法的谱估计性能,能够在噪声环境下获得准确的间谐波参数估计。     

改进谱减法结合神经网络的语音增强研究

背景噪声是通信系统噪声干扰的来源之一,语音增强可以降低乃至消除噪声干扰,进而提高语音的可懂度.为了减小复杂噪声环境下谱减法引发的音乐噪声,采取正交的多窗谱估计对语音功率谱平滑处理,有效的减小了信息丢失和估计波动.利用自适应谱减系数调整谱增益和谱下限来控制残留噪声,利用优化的IMCRA算法对噪声及时更新来判决语音段和静音段,同时借助特性良好的BP神经网络方法进行训练,语音和噪声谱通过谱减后,波形重构获取增强的语音信号.仿真结果表明语音降噪效果好、可懂度高.     

改进TF-GSC和改进后置滤波语音增强算法

由于声学环境中噪声的复杂性和不确定性，传统的多通道语音增强算法对于噪声的抑制效果不足，从而导致了较差的听觉体验。针对这一问题，提出了一种改进TF GSC和改进后置滤波语音增强算法。算法使用最大似然法得到目标语音信号和噪声信号的功率谱密度，然后使用信号功率谱密度比值得到的变步长归一化最小均方算法来改进TF GSC。还提出了联合信号功率谱密度比值和先验信噪比估计语音存在概率的改进最优修正对数幅度谱估计器。不同信噪比环境下的仿真实验表明，本文提出的算法可以有效地滤除相干噪声和非相干噪声，与其他算法相比，增强后的语音信号具有更高的信噪比和语音质量。     

A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

This study addresses the problem of speech quality enhancement by adaptive and nonadaptive filtering algorithms. The well‐known two‐microphone forward blind source separation (TM‐FBSS) structure has been largely studied in the literature. Several two‐microphone algorithms combined with TM‐FBSS have been recently proposed. In this study, we propose 2 contributions: In the first, a new two‐microphone Gauss‐Seidel pseudo affine projection (TM‐GSPAP) algorithm is combined with TM‐FBSS. In the second, we propose to use the new TM‐GSPAP algorithm in speech enhancement. Furthermore, we show the efficiency of the proposed TM‐GSPAP algorithm in speech enhancement when highly noisy observations are available. To validate the good performances of our algorithm, we have evaluated the adaptive filtering properties in computational complexity and convergence speed performance by system mismatch criteria. A fair comparison with adaptive and nonadaptive noise reduction algorithms are also presented. The adaptive algorithms are the well‐known two‐microphone normalized least mean square algorithm, and the recently published two‐microphone pseudo affine projection algorithm. The nonadaptive algorithms are the one‐microphone spectral subtraction and the two‐microphone Wiener filter algorithm. We evalute the quality of the output speech signal in each algorithm by several objective and subjective criteria as the segmental signal‐to‐noise ratio, cepstral distance, perceptual evaluation of speech quality, and the mean opinion score. Finally, we validate the superior performances of the proposed algorithm with physically measured signals.     

Fast estimation of voltage and current phasors in power networksusing an adaptive neural network

A new algorithm for the estimation of parameters of voltage or current waveform of power networks contaminated by noise is proposed. The problem of estimation is formulated by using an adaptive neural network consisting of linear adaptive neurons called adaline. The learning parameters of the adaline are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation, rather than to minimize an error function. Illustrative computer simulation results confirm the validity and accurate performance of the proposed method. Laboratory test results are also presented in this paper to support the effectiveness of the proposed approach in tracking the waveforms in real-time     

小波包结点阈值自适应消噪法

当噪声为时变的时候,传统的小波阈值消噪方法效果很有限:当采用软阈值消噪时,总体效果较好,但当含噪信号很不规则时显得过于光滑;当采用硬阈值消噪时,消噪效果不理想,信号含有明显的噪声。为克服上述缺陷,本文提出基于"自适应阈值消噪思想"的小波包结点阈值消噪法。首先,我们给出了小波包结点阈值的定义,这种阈值取法突破了高斯白噪声和平稳噪声的局限性;其次,我们提出了基于谱熵的噪声估计法用以替代传统的中值绝对值(MAD)噪声估计法,这种噪声估计方法与实际应用环境相符,适用于非平稳噪声和有色噪声。仿真实验证实,相比于常规小波消噪算法,结点阈值法和基于谱熵的噪声估计法在白噪声环境下具有较好的消噪效果。     

氧化锌避雷器泄漏电流的全相位FFT分析

通过检测氧化锌避雷器泄漏电流,经过谐波分析提取泄漏电流基波和各次谐波参数,依据电流阻性分量可判断该装置在电网中的运行情况。为解决快速傅里叶变换(FFT)进行谐波分析时数据截断引起数字信号处理性能下降的问题,选用一种考虑包含某样点所有可能数据截断情况的全相位FFT分析方法,再基于此分析方法选用全相位时移相位差校正算法,同时利用窗谱函数推导出校正公式。仿真结果表明,该算法对比基于FFT的比值公式校正算法在无噪和有噪(50 dB)情况下谐波分析精度分别提高了4～5个数量级和1～2个数量级,且对相对小信号谐波分量的频率估计偏差不超过0.6 Hz,相位估计偏差不超过0.5°。该算法具有相位不变性和频谱泄漏抑制能力,实现了检测精度的提高和小信号参数的估计,并在泄漏电流检测系统中得到验证。     

基于TLS-ESPRIT算法和自适应神经网络的间谐波分析

提出一种基于总体最小二乘法-旋转不变技术信号参数估计(TLS-ESPRIT)算法的间谐波参数估计方法。首先通过TLS-ESPRIT算法准确估计电网信号中谐波和间谐波的个数及频率,然后应用自适应神经网络来估计其幅值和相位。自适应神经网络用于谐波分析时,不需要事先进行训练,具有实现简单快速的优点。仿真结果表明该算法无需数据同步采样、频率分辨率高、抗干扰性强、谐波和间谐波参数估计准确。     

基于自适应SRUKF算法的电力系统动态谐波状态估计

针对传统无迹卡尔曼滤波(unscented kalman filter, UKF)谐波状态估计算法存在时变噪声和异常数据时估计准确度较差的情况,提出了一种基于自适应平方根无迹卡尔曼滤波(square-root UKF, SRUKF)的电力系统谐波状态估计算法。首先,针对时变噪声干扰,引入改进的Sage-Husa噪声估计方法实时估计噪声协方差。其次,针对异常数据干扰,引入异常数据修正方法,通过修正系数来降低异常数据对状态估计结果的影响。最后,通过搭建IEEE14节点系统验证自适应SRUKF算法的估计性能,能够有效地应用于电力系统的动态谐波状态估计。仿真结果表明,该算法在时变噪声和异常数据干扰时仍具有良好的估计性能。     

Estimation of frequency and its rate of change for applications in power systems

A new method for estimation of power frequency and its rate of change is presented. Unlike conventional methods which are based on the concept of linearization, the proposed scheme accommodates the inherent nonlinearity of the frequency estimation problem. This makes the method capable of providing a fast and accurate estimate of the frequency when its deviation from the nominal value is incremental or large. The estimator is based on a newly developed quadrature phase-locked loop concept. The method is highly immune to noise and distortions. The estimator performance is robust with respect to the parameters of its structure. Structural simplicity and performance robustness are other salient features of the method.     

基于同步挤压小波变换的电力系统时变谐波检测

针对噪声影响下时变谐波检测问题,提出基于同步挤压小波变换(SWT)的检测方法。首先利用SWT算法将混有噪声的时变谐波信号分解为不同的内蕴模态函数分量(IMTs),进而提取出所含谐波分量,然后对每个谐波分量进行Hilbert变换,得到每个本征函数的瞬时频率和幅值及谐波扰动的起止时刻。基于SWT准确的模态分离能力,实现了各谐波的瞬时频率、瞬时幅值和谐波扰动的起止时刻等参数较高精度的获取。与传统的希尔伯特-黄(HHT)方法相比,该方法对于谐波的分离能力更强,对噪声有更强的鲁棒性。仿真及实际数据表明,所提方法在噪声环境下依然能精确提取各个模态,验证了该方法在时变谐波检测分析中的有效性。     

基于改进鲁棒自适应UKF的配电网动态状态估计方法

配电网动态状态估计中状态方程的过程噪声统计参数是未知而且时变的,因此在状态估计过程中需要在线对过程噪声统计参数进行实时估计,而且不准确的噪声参数将会导致无迹卡尔曼滤波器的滤波性能下降甚至滤波发散。文中研究了基于改进鲁棒自适应无迹卡尔曼滤波器的配电网动态状态估计方法,其噪声参数统计估值器由一个有偏的和一个无偏的估值器组成,可以提高在状态估计过程中噪声参数估计的准确性,同时确保过程噪声方差矩阵的半正定性,从而保证算法的鲁棒性。通过对IEEE 33节点系统进行仿真验证,结果表明所提方法在系统平稳运行、负荷发生剧烈变动或者初始噪声参数值设置不当的情况下,均能保证较高的状态估计精度。     

Reducing correlated noise in digital hearing aids

The intelligibility of speech in communication systems is generally reduced by interfering noise. This interference, which can take the form of environmental noise, reverberation, competing speech, or electronic channel noise, reduces intelligibility by masking the signal of interest. The reduction in intelligibility is particularly troublesome for listeners with hearing impairments, who have greater difficulty understanding speech in the presence of noise than do normal-hearing listeners. Numerous digital signal processing (DSP)-based speech enhancement systems have been proposed to improve intelligibility in the presence of noise. Several of these systems have difficulty distinguishing between noise and consonants, and consequently attenuate both. Other methods, which use imprecise estimates of the noise, create audible artifacts that further mask consonants. The objective of the present study is to develop a new noise-reduction method that can reduce additive noise without impairing intelligibility. The new method could be used to improve intelligibility in a wide variety of applications, with special attention given to digital hearing aids and other portable communication systems (e.g., cellular telephones). Here, the authors present a new wavelet-based method for reducing correlated noise in noisy speech signals. The authors provide background information on the intelligibility problem and on previous attempts to address it. A theoretical framework is then proposed for reduction of correlated noise, along with some preliminary experimental results     

基于无迹卡尔曼滤波的电力系统抗差动态估计

为解决电力系统动态状态估计准确性易受量测不良数据影响的问题,提出基于无迹卡尔曼滤波(Unscented Kalman Filter,UKF)的电力系统抗差动态估计方法。在预测过程中引入时变噪声估计器处理未知系统噪声;利用新息向量判断量测是否存在异常,并使用基于测点正常率最大的静态估计方法辨识不良数据;然后构建更新因子矩阵降低不良数据在动态估计更新过程中的影响。将算法运用于IEEE 14节点标准系统中,仿真结果表明该方法估计结果准确且抗差效果良好。     

联合频谱映射与掩蔽估计的协作式语音增强方法

为提高目前基于掩蔽与基于频谱映射的语音增强方法性能上界以及复杂环境下的泛化能力,提出了一种在联合复频谱 与复掩蔽学习框架下的协作式单通道语音增强方法。 该方法采用编码器-双分支解码器结构,在编解码部分设计了一种交互协 作学习单元(ICU)来监督交互语音信息流,并提供有效的潜在特征空间;中间层则是设计出一种多尺度融合 Transformer,以少 量参数在空间-通道维度上多尺度地提取细节信息后融合输出,同时对语音子频带与全频带信息建模。 在大、小数据集与 115 种噪声环境下进行实验,结果表明该方法仅以 0. 57 M 的参数量,取得比大部分先进且相关方法更优的主、客观指标,具有良好 的鲁棒性与有效性。     

基于线性预测与归一化互相关的基音检测

基音是语音信号分析的重要特征参数之一,在众多领域有广泛的应用。由于传统的基音检测算法在信噪比较低时检测效果较差,提出了一种新型的基于线性预测与归一化互相关的基音检测算法,该算法利用线性预测分析,消除噪声和声道冲击响应对基音检测的影响,采用归一化互相关系数选取基音周期,并采取了动态规划算法对基音周期进行平滑。仿真实验表明,方法性能优于传统自相关法和平均幅度差法,尤其在信噪比较低的带噪语音音环境下也具有很好的鲁棒性。     

基于短时傅里叶变换的局部放电窄带干扰抑制

为有效抑制局部放电(PD)信号中周期性窄带干扰,文中提出短时傅里叶变换和矩阵束相结合的局部放电窄带干扰抑制方法。该方法利用短时傅里叶变换分析染噪PD信号,得到窄带干扰数目,同时分离出染噪PD信号中信号帧和噪声帧;采用矩阵束算法在噪声帧中对窄带干扰参数进行估计,重构出全时段的窄带干扰,实现局部放电的窄带干扰抑制。仿真及实测染噪PD信号的去噪效果表明,与传统的傅里叶级数法和局部能量比法相比,所提方法去噪后的残余噪声更小,对染噪PD信号中窄带干扰有很好的抑制效果。     

基于在线参数辨识和ASR-UKF的锂离子电容SOC估计

以一种新型混合型超级电容器——锂离子电容为研究对象,针对其在混合动力机车应用中的SOC估计问题,建立锂离子电容的二阶等效电路模型,采用带遗忘因子的递推最小二乘法(FFRLS)和自适应平方根无迹卡尔曼滤波算法(ASR-UKF)交叉联合的方法对锂离子超级电容的荷电状态(SOC)进行估算.FFRLS可以对动态变化的模型参数进...     

基于自适应容积卡尔曼滤波的动态谐波检测

对电网中的谐波进行实时、准确的检测是有效治理谐波的前提。针对某些运行工况下电网中出现的动态谐波,提出了一种基于自适应容积卡尔曼滤波的动态谐波检测算法估计谐波信号的幅值和相角。首先针对传统卡尔曼滤波处理非线性关系上的局限性,利用容积卡尔曼滤波不需要任何线性化关系的特性估计谐波的状态向量和误差偏差矩阵,然后引入噪声估值遗忘因子来实时更新系统的噪声矩阵方程。最后通过对比实验,验证了该算法在动态谐波检测上的优越性能,并将其应用于有源滤波器的谐波检测中。