A method of coding LSP residual signals using wavelets for speech synthesis

This paper presents a method of using wavelet analysis for speech coding and synthesis by rule. It is a coding system where LSP residual signal is transformed into wave‐let coefficients. As wavelet analysis is implemented effectively by filter banks, our method requires less computation than multipulse coding and others where complicated prediction procedures are essential. To achieve good‐quality speech at low bit rates, we verified allocation of different bits onto the wavelet coefficients, with more bits in lower frequencies, and less in higher. The synthesized speech by Haar wavelet with 16.538 kbit/s has nearly the same perceptual quality with 6 bits µlog PCM (66.15 kbit/s). We are convinced that the coding method of LSP residual signals using wavelet analysis is an effective approach to synthesize speech. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 148(3): 54–61, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10326     

Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation

Recently, sparsity‐aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm for various sparse signals, such as the well‐known zero‐attracting LMS (ZA‐LMS) algorithm and its reweighted ZA‐LMS (RZA‐LMS) algorithm. To utilize the sparsity of the channels in wireless communication and one of the inherent advantages of the RZA‐LMS algorithm, we propose an adaptive reweighted zero‐attracting sigmoid functioned variable‐step‐size LMS (ARZA‐SVSS‐LMS) algorithm by the use of variable‐step‐size techniques and parameter adjustment method. As a result, the proposed ARZA‐SVSS‐LMS algorithm can achieve faster convergence speed and better steady‐state performance, which are verified in a sparse channel and compared with those of other popular LMS algorithms. The simulation results show that the proposed ARZA‐SVSS‐LMS algorithm outperforms the standard LMS algorithm and the previously proposed sparsity‐aware algorithms for dealing with sparse signals. Copyright © 2015 John Wiley & Sons, Ltd.     

Sparse leaky‐LMS algorithm for system identification and its convergence analysis

In this paper, a novel adaptive filter for sparse systems is proposed. The proposed algorithm incorporates a log‐sum penalty into the cost function of the standard leaky least mean square (LMS) algorithm, which results in a shrinkage in the update equation. This shrinkage, in turn, enhances the performance of the adaptive filter, especially, when the majority of unknown system coefficients are zero. Convergence analysis of the proposed algorithm is presented, and a stability criterion for the algorithm is derived. This algorithm is given a name of zero‐attracting leaky‐LMS (ZA‐LLMS) algorithm. The performance of the proposed ZA‐LLMS algorithm is compared to those of the standard leaky‐LMS and ZA‐LMS algorithms in sparse system identification settings, and it shows superior performance compared to the aforementioned algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Blind separation of convolutive mixtures of cyclostationary signals

An adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary signals is proposed. The algorithm is derived by applying natural gradient iterative learning to a novel cost function which is defined according to the wide sense cyclostationarity of signals and can be deemed as a new member of the family of natural gradient algorithms for convolutive mixtures. A method based on estimating the cycle frequencies required for practical implementation of the proposed algorithm is presented. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures. Copyright © 2004 John Wiley & Sons, Ltd.     

A simple variable step size LMS adaptive algorithm

A new LMS based variable step size adaptive algorithm is presented. The step size is incremented or decremented by a small positive value, whenever the instantaneous error is positive or negative, respectively. The algorithm is simple, robust and efficient. It is characterized by fast convergence and low steady state mean squared error. The performance of the algorithm is analysed for a stationary zero‐mean white‐Gaussian input. MC simulations are provided to demonstrate its improved performance over the conventional LMS (Proc. IEEE 1976; 64 :1151–1162) and some other variable step size adaptive algorithms (IEEE Trans. Signal Process. 1992; 40 :1633–1642; IEEE Trans. Signal Process. 1997; 45 :631–639) within a range of statistical environments. For a non‐stationary input, the proposed algorithm behaves similar to these algorithms. A modified version of the algorithm is presented to perform in the presence of abrupt changes. Copyright © 2004 John Wiley & Sons, Ltd.     

Adaptive least mean squares block Volterra filters

Adaptive filtering has found many applications in situations where the underlying signals are changing or unknown. While linear filters are simple from implementation and conceptual points of view, many signals are non‐linear in nature. Non‐linear filters based on truncated Volterra expansions can effectively model a large number of systems. Unfortunately, the resulting input auto‐moment matrix is ill conditioned, which results in a slow convergence rate. This paper proposes a class of block adaptive Volterra filters in which the input sequences are Hadamard transformed to improve the condition number of the input auto‐moment matrix and consequently improve the convergence rate. This is achieved by the decorrelation effect produced by the orthogonality of the transform. Since Hadamard transformation employs only ±1's, the additional required computational and implementation burdens are few. The effect of additive white Gaussian noise is introduced. Simulation experiments are given to illustrate the improved performance of the proposed method over the conventional Volterra LMS method. Copyright © 2001 John Wiley & Sons, Ltd.     

Extension of LMS stability condition over a wide set of signals

A sufficient condition for least mean squares (LMS) algorithm stability with a small set of assumptions is derived in this paper. The derivation is not, contrary to the majority of currently known conditions, based on the independence assumption or other statistic properties of the input signals. Moreover, it does not make use of the small‐step‐size assumption, neither does it assume the input signals are stationary. Instead, it uses a theory of discrete systems and properties of a discrete state‐space matrix. Therefore, the result can be applied to a wide set of signals, including deterministic and nonstationary signals. The location of all eigenvalues of the matrix responsible for the LMS algorithm stability has been calculated. Simulation experiments, where the step size reaches a couple of hundreds without loss of stability, are shown to support the theory. On the other hand, simulation where the calculations based on the small‐step‐size theory provide a too large estimation of the upper bound for the step size, while the new condition gives a proper solution, is also presented. Therefore, the new condition may be used in cases where fast adaptation is necessary and when the independence theory or the small‐step‐size assumptions do not hold. Copyright © 2014 John Wiley & Sons, Ltd.     

Higher‐order finite‐difference schemes with reduced dispersion errors for accurate time‐domain electromagnetic simulations

The potential for developing higher‐order finite‐difference time‐domain (FDTD) schemes with reduced phase errors is investigated in the present paper. Using the classic (2,4) FDTD method as the basis of this study, electromagnetic wave propagation is accurately reproduced in the discretized space by replacing isotropic materials with modified, anisotropic in general, ones. The use of such artificial materials improves the simulation's precision significantly around a specific frequency, yet the overall error remains small at a considerably wide bandwidth; therefore, this algorithm can be useful for wideband problems as well. Additionally, it is shown that an even better single‐frequency performance can be attained, when the modified materials are combined with systematically calculated spatial operators. Pursuing a more wideband enhancement of the (2,4) technique, a version realizing more accurate results at almost all frequencies that can be coupled in a staggered grid is derived. Furthermore, novel spatial operators are introduced, with the distinct feature of using extended stencils in more than one directions. It turns out that when such operators are incorporated, a scheme that combines the aforementioned features can be obtained. The theoretical findings of this investigation are verified in a sequence of numerical tests, involving free‐space and guided‐wave propagation, as well as the determination of a cavity's resonant frequencies. Copyright © 2004 John Wiley & Sons, Ltd.     

基于小波变换的改进LMS算法在异步电动机轴承故障诊断中应用

基于电机定子电流信号分析方法的异步电动机轴承故障检测中,计及实际电动机供电电压谐波和三相电压不平衡等外部因素的情况下,如何实现轴承故障的可靠检测一直是电动机故障检测领域的难题.对传统的定子电流频谱分析方法进行了深入研究,讨论了传统最小均方算法(LMS)自适应滤渡方法在信号处理中的不足.在此基础上,提出了将小渡分析、连续细化傅里叶变换和改进LMS自适应滤波方法有机结合的异步电动机轴承故障检测新方法.该方法能够正确判断轴承故障特征频率分量,从而提高异步电动机轴承故障诊断效果,实现轴承故障的可靠检测.实验结果表明了该方法的有效性.     

A joint echo cancellation algorithm for quick suppression of howls in hearing aids

When the echo path of a hearing aid suddenly changes, howls easily occur. To quickly suppress the howls, a joint echo cancellation (JEC ) algorithm, which combines the variable step normalized least mean square (VNLMS ) algorithm with the notch filter algorithm, is proposed. According to whether the hearing aid howls or not, different strategies are used. First, when there are no howls, the echo signal is estimated using VNLMS and the step factor is computed according to three types of filter states, which are defined based on the normalized distance between the short‐term average and the long‐term average of the filter coefficients. Then, different step factors are used for different states. Second, when there are howls, the update of VNLMS is frozen to stabilize the howl frequency. To improve the detection accuracy, a howling detection algorithm based on the zoom‐fast Fourier transformation (ZoomFFT ) is proposed. The ZoomFFT algorithm can analyze the spectrum of a narrowband signal in a specified high sampling frequency. Then, the notch filters based on the estimated howl frequencies are dynamically generated to restrain the howls. Finally, when the howls are suppressed, VNLMS is reactivated. Compared to other echo cancellation algorithms, the proposed algorithm can quickly suppress the howls, and JEC has the best comprehensive performance. Furthermore, the quality of the processed speech is high, and the operation time is short. Thus, the proposed algorithm is suitable for low‐power‐consumption and small‐volume products such as hearing aids. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于Mallat算法和快速傅里叶变换的电能质量分析方法

提出了一种基于Mallat算法和快速傅里叶变换的电能质量分析方法。将小波消噪应用于采样信号,根据信号的突变点检测结果,将采用Mallat分解算法得到的第一层和第二层高频系数作为区分稳态和非稳态扰动的判据,进而求出扰动的持续时间。根据多分辨分析的频带划分原理,采用Mallat重构算法提取出了暂态扰动波形,并编制了可准确判别电压骤降、骤升和断电等短期变化扰动的识别子程序。对于稳态扰动,提出可将快速傅里叶变换作为区分谐波和闪变的一种手段。Matlab的仿真结果验证了该方法的准确性和有效性。     

一种滤除衰减直流分量的全波傅氏改进算法研究

全波傅氏算法在提取故障电流中基波分量时受衰减直流分量的影响较大。针对此问题,提出了一种滤除衰减直流分量的全波傅氏改进算法,给出新型衰减直流分量参数估算方法的公式推导。首先利用一个周波内的采样值求出故障电流中衰减直流分量的初始幅值和衰减时间常数,用采样值减去衰减直流分量值得到修正后的采样值,再利用全波傅氏算法计算出基波分量。分别采用静态模型信号、PSCAD/EMTDC仿真信号检验了该算法的性能。仿真结果表明,所提出的算法能够有效地减少衰减直流分量的影响。与一般改进算法相比,所提算法仅需要一个周波的采样数据,计算量小,计算的基波分量准确性高。     

Frequency measurement of distorted signals using Fourier and zero crossing techniques

An approach to the design of a digital algorithm for network frequency estimation is proposed. The algorithm is derived by using the Fourier and zero crossing techniques. The Fourier method is used for digital filtering and the zero crossing technique is applied to the cosine or sine components of the original signal, which is usually corrupted by higher harmonics. The algorithm showed a very high level of robustness as well as a high measurement accuracy over a wide range of frequency changes. It can be used for frequency tracking in power networks when higher harmonics are present in the voltage or current signals. The theoretical basis and practical implementation of the technique are described. The performance of the developed algorithm has been verified by the computer simulations, and the field and laboratory tests.     

基于ESPRIT的谐波和间谐波参数估计方法

为了准确地获得信号中谐波、间谐波成分的频率和幅值等参数,提出了一种新的检测算法,即ESPRIT(Estimation of Signal Parameters via Rotational Invariance Techniques)。此算法是一种基于子空间技术的高分辨率检测方法,它把信号分解为信号子空间和噪声子空间,能够精确地估计出被噪声污染的正弦信号的频率,幅值等信息,克服了传统FFT算法分辨率的限制。仿真结果表明此算法能够在较短的信号长度内准确检测出信号各个谐波和间谐波成分,证明了此算法的正确性。     

Modeling the resonant frequency of compact microstrip antenna by the PSO‐based SVM with the hybrid kernel function

A methodology based on the support vector machine (SVM) combined with a hybrid kernel function (HKF) for accurately modeling the resonant frequencies of the compact microstrip antenna (MSA) is presented and dedicated to reduce the number of samples and simplify the structure when predicting the resonant frequency of the compact MSA by artificial neural network. The parameters of the SVMs and weight coefficients of the HKF are optimized by means of particle swarm optimization algorithm. In addition, two different kernel functions (KFs), namely polynomial KF (a kind of global KF) and Cauchy KF (a kind of local KF), are employed to overcome the disadvantages of traditional KF. The proposed method is validated by the UCI database. The evaluation results show that the HKF can improve the learning ability and generalization ability of the SVM. Furthermore, the resonant frequencies of a planar inverted F‐shaped antenna and an L‐shaped MSA are modeled by the proposed method. Predictive results with high accuracy demonstrate that the particle swarm optimization‐based SVM with the HKF can improve the prediction accuracy for a small dataset. Copyright © 2016 John Wiley & Sons, Ltd.     

基于变分模态分解的电能质量扰动检测新方法

为了更加准确地提取扰动信号特征,提出了基于变分模态分解(VMD)的电能质量扰动检测新方法。该方法由VMD和希尔伯特变换(HT)2个部分组成。首先,对扰动信号进行傅里叶变换以确定VMD的预设分解尺度;然后,利用VMD将扰动信号分解为系列调幅-调频函数之和;最后,对每个调幅-调频函数进行HT,求取瞬时幅值和瞬时频率,进而确定扰动信号特征。较之希尔伯特-黄变换和局部均值分解方法,VMD方法不仅可分析不同时间支集的扰动信号,处理复合扰动和频率相近的奇数次谐波,也不存在模态混叠,获取的瞬时幅值和瞬时频率更加准确。仿真信号和变电站电容器组投入时的电压信号分析结果证明了所提方法的可行性和有效性。     

横向时延滤波和IQ正交滤波的滤波效果仿真

由于横向时延滤波和IQ正交滤波在稳定性、收敛速度和误差率等方面的性能差异对于最小均方算法(LMS,Least Mean Squares)在工程上的实际应用具有参考意义,采用数值仿真的方法模拟了两者实现自适应最小均方算法的具体过程,并对比分析了它们的滤波性能。仿真结果显示IQ正交滤波具有更高的稳定性、更快的收敛速度、更小的误差率,其滤波性能优于横向时延滤波。IQ正交滤波器的阶数为2阶,而横向时延滤波器的阶数通常>10,滤波器阶数越多引入的权噪声也会相对增加,对于稳定性、收敛速度和误差率都是不利的,因此实际应用中优先选择阶数较少的IQ正交滤波器。     

An easy demonstration of the optimum value of the adaptationconstant in the LMS algorithm [FIR filter theory]

The least mean squares (LMS) is the most widely used algorithm among those proposed to adapt the coefficients of an FIR filter in order to minimize the mean-square error (MSE) between its output and the desired signal. Since the introduction of the LMS algorithm, many variants have been proposed to improve its performance. Doubtless, the most popular is the normalized LMS algorithm, which uses a value for the adaptation constant that assures the fastest convergence. This correspondence shows a new demonstration of the algorithm based on a mathematical approach easier than that usually proposed     

A new blind deconvolution algorithm based on the probability distribution method

Blind deconvolution is a method of recovering transmitted signals from only received signals. The probability distribution method is one of the blind deconvolution methods. This method has two problems: it has slower convergence and its reliability is lower. In this paper, we propose a new algorithm for solving these two problems. The proposed algorithm is as follows. (1) It is based on the adaptive processing with each sample. (2) Kurtosis is adaptively estimated by recovered signals with each sample. (3) Cost function is decided by kurtosis. (4) Transmitted signals are recovered by received signals using decided cost function on the sample time. We confirm the validity of the new algorithm by computer simulation. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(1): 56–65, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20317     

采用Prony算法的谐波和间谐波分析

传统的谐波检测方法——快速傅里叶变换存在栅栏和频谱泄露现象,因而对谐波和间谐波信号不能实现精确的检测。采用扩展Prony算法检测信号的谐波和间谐波,能一次性精确地检测到整数和非整数次谐波的相位、振幅、频率,为电力系统整次和非整次谐波检测提供了一条新的途径。数值仿真验证了该算法的有效性。