一种混叠语音清浊音判断方法

提出了一种混叠语音清浊音判断方法。该方法在分频带自相关函数的混叠语音基频分离提取算法中，引入了双周期度及频带组的分布度两个参数用于对混叠语音清浊判断，从而使基于分频带自相关函数的混叠语音基频分离提取方法不再局限于浊音，可推广到清音信号。实验结果证明算法有一定的清浊音判断能力。     

基于协方差矩阵的盲分离算法

提出了一种新的实时线性混叠信号的盲分离算法，该算法利用信号相互独立时其协方差矩阵的对角化特征作为分离准则，采用最速下降法进行分离。该算法对源信号和混叠矩阵没有过多要求且计算量不大，理论分析与仿真结果表明，该算法具有很好的分离效果。     

一种基于语音场景分析的听觉模型

从语音场景分析的角度提出了一种听觉模型。文中采用独立分量分析中的FastICA算法对场景中的混叠语音进行分离,完成了听觉分辨功能。对于听觉功能中的说话人验证问题,结合优化时频分布和距离测度方法完成了验证过程。通过对实验结果的详细评估说明提出的模型具有较好的处理效果,很好地模拟了人类听觉功能,误判率仅为2.4%。     

蚁群聚类的欠定盲源分离方法

利用欠定盲源分离情况下稀疏源信号具有直线聚类的特点,提出了一种估计混叠矩阵的新方法。通过对混叠信号进行标准化处理,使混叠信号形成球形簇,将线性聚类转变成致密聚类;利用蚁群聚类算法对其进行搜索得到聚类中心,从而获得对混叠矩阵的精确估计。该方法能实现源信号数目未知情况下的欠定盲源分离,且能推广到三路或更多路观测信号的情况。对语音信号的仿真结果证明,该方法能精确地分离和恢复原始信号。     

小波消噪和ICA在语音信号分离中的应用

为了消除语音信号分离中仍存在的部分混叠声音,提出一种基于小波消噪和独立分量分析（ICA）结合的信号分离方法。该方法将小波变换和独立分量分析结合,利用小波变换的去噪作用,滤除原始语音信号中的噪声后作为ICA的输入信号,采用FastICA算法在小波域进行独立分量分析,对输入信号实施分离。实验结果表明,该方法大大调高了传统独立分量分析对语音信号的分离效果。     

基于单周控制的三相桥式双频逆变器仿真研究

研究了基于双频的三相桥式逆变器拓扑结构,该拓扑由两个传统的三相桥式逆变器级联而成,其中一个工作在低频状态,另一个工作于高频状态,两单元功能相对分离。对高频单元采用单周控制,对低频单元采用电流滞环控制,利用Matlab/Simulink建立了仿真模型。仿真结果表明,该拓扑对降低开关损耗、电流总谐波畸变率、提高系统响应速度具有很好的作用。     

单传感器中两路同频调幅信号的盲分离算法

首先描述了同频调幅信号的射频多相位混叠模型，提出了一种多相位映射的新方法。该方法可以在单载频单传感器情况下实现至多两路信号混叠矩阵的重建，然后采用独立分量分析的相关算法实现了信号的盲分离。仿真结果表明：本算法不仅能解决两路混叠调幅信号的分离问题，而且能有效地抑制带通强噪声干扰。实验中被带通噪声完全淹没的信号最终的分离信噪比均值达到50dB。     

基于能量分离的混叠语音干扰滤除方法研究

背景噪声中的其他语音干扰在时域和频域上与有用语音完全混叠在一起,用普通的频分技术无法将其去除。本文从能量角度入手,利用调频-调幅模型和共振峰特征,滤除混叠在有用语音中的含绝大部分能量的元音干扰。最后对该方法在MATLAB中进行了仿真验证,并给出了仿真结果。     

基于PID神经网络的后非线性盲源分离算法

PID神经网络是一种新型的前向神经元网络，它的隐层单元包含比例（P）、积分（1）、微分（D）元，各层神经元个数、连接方式、连接权初值均按PID控制规律的基本原则确定。本文研究了一种新的后非线性盲源分离算法，用最大熵值方法推导了PID神经网络算法的后非线性分离学习公式，该算法可用于线性或后非线性的混叠信号。对输入2个混叠信号时，用单个PI神经网络分离；对输入3个混叠信号时，用单个PID神经网络分离；对输入更多的混叠信号时，可采用多个独立的PID神经网络来分离。仿真结果验证了单个PID神经网络算法，能分离线性或后非线性混叠信号。     

基于双基频多带激励分离模型的元音分离

听觉场景分析(AuditorySceneAnalysis,ASA)是一种模仿人的听觉特性实现对混叠声音信号进行分离的方法。作为ASA的基础研究,论文针对各种ASA系统存在的相近频带信号无法有效分离问题,提出了一种新的基于双基频多带激励分离模型的元音分离系统,该系统利用两语音信号的基音轨迹特性提取多带激励分离模型中两基频对应的语音的各次谐波参数,将两组参数代入多带激励合成模型从而得到两个分离的语音信号。文中给出了算法的原理与具体描述。仿真实验结果表明,系统对存在基音频率差异的元音信号能实现有效的分离。     

Monaural speech segregation based on pitch tracking and amplitude modulation

Segregating speech from one monaural recording has proven to be very challenging. Monaural segregation of voiced speech has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with the high-frequency part of speech. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. We propose a novel system for voiced speech segregation that segregates resolved and unresolved harmonics differently. For resolved harmonics, the system generates segments based on temporal continuity and cross-channel correlation, and groups them according to their periodicities. For unresolved harmonics, it generates segments based on common amplitude modulation (AM) in addition to temporal continuity and groups them according to AM rates. Underlying the segregation process is a pitch contour that is first estimated from speech segregated according to dominant pitch and then adjusted according to psychoacoustic constraints. Our system is systematically evaluated and compared with pervious systems, and it yields substantially better performance, especially for the high-frequency part of speech.     

The use of three- and four-dimensional surface harmonics for rigidand nonrigid shape recovery and representation

The use of spherical harmonics for rigid and nonrigid shape representation is well known. This paper extends the method to surface harmonics defined on domains other than the sphere and to four-dimensional spherical harmonics. These harmonics enable us to represent shapes which cannot be represented as a global function in spherical coordinates, but can be in other coordinate systems. Prolate and oblate spheroidal harmonics and cylindrical harmonics are examples of surface harmonics which we find useful. Nonrigid shapes are represented as functions of space and time either by including the time-dependence as a separate factor or by using four-dimensional spherical harmonics. This paper compares the errors of fitting various surface harmonics to an assortment of synthetic and real data samples, both rigid and nonrigid. In all cases we use a linear least-squares approach to find the best fit to given range data. It is found that for some shapes there is a variation among geometries in the number of harmonics functions needed to achieve a desired accuracy. In particular, it was found that four-dimensional spherical harmonics provide an improved model of the motion of the left ventricle of the heart     

基于EEMD-ICA与SVD的电网谐波检测方法研究

论文将EEMD-ICA技术与SVD相结合,提出基于EEMD-SVD-ICA算法的单通道电网电压谐波分离方法,与现有单通道谐波分离方法相比具有无需源信号先验信息,可分离非平稳信号谐波,算法简单等优点。EEMD方法将单通道信号分解为多路互相正交的本征模态函数分量(IMFs),然后采用SVD代替PCA方法进行数据降维,再运用基于负熵的固定点独立成分分析(FastICA)方法提取IMFs独立分量,实现单通道电网电压谐波分离。对模拟信号进行谐波分离,验证所提方法在该领域的应用的可行性,仿真结果表明论文所提方法不仅能够分离电网电压的谐波,并且对频率小于50Hz的间谐波也有很好的分离效果。     

电弧炉电气系统谐波分析的仿真研究

交流电弧炉是一种不平衡、非线性、时变负载,在炼钢过程中其电流会产生非正弦畸变和各次谐波,使得电网电能质量变差,通过对电弧炉电气系统的谐波分析,有利于采取合适的谐波抑制措施,改善电网电能的质量.本文采取在MATLAB 6.5/Simulink环境下搭建交流电弧炉系统仿真模型的方法,通过仿真对电弧炉电气系统进行谐波分析.仿真结果表明,电弧炉的谐波电流成份主要为2～7次,与实际系统频谱分析仪实测的结果定性性质一样.     

一种新型电网谐波在线监测系统的研究

本文主要提出了一种新型电网谐波在线监测系统模型，其在线监测部分主要利用模拟并行测量的基本原理．构造了一个用于电网谐波在线监测的特殊多层前向神经网络。并对其进行了仿真．仿真结果表明本文所提出的基于人工神经网络对电网谐波在线监测的方法是可行的、正确的和有效的。     

并联混合型有源电力滤波器的主要参数设计和仿真研究

并联混合型有源电力滤波器可较好地补偿电流源型谐波源产生的谐波。其交流侧电感、直流侧电容的设计对系统的补偿效果至关重要。文章在理论分析的基础上 ,利用MatLab中的电力系统仿真工具箱对并联混合型有源电力滤波器进行了建模和仿真研究。仿真结果表明 :并联混合型有源电力滤波器可对电力系统中的谐波、无功电流分量进行较好的补偿     

基于谐波探测理论功率放大器线性化仿真研究

Volterra级数是一种用于解决非线性问题数学模型,在功率放大器线性化领域中,其庞大的计算难度限制了实际线性化处理的效果。为了解决Volterra级数计算量过大的问题,使用谐波探测方法替代Volterra级数,使用多个简单多项式对功率放大器复杂的记忆非线性特性进行建模,结合该模型与前馈线性化结构,提出了一个基于谐波检测的数字前馈结构。该数字前馈方法避免了前馈方法中时延因素对于功率放大器线性化效果的影响。仿真中,上述方法提供了平均20dB的抑制效果,验证了谐波探测理论应用于功率放大器线性化领域的可行性。     

Harmonic retrieval by period blind source extraction method: Model and algorithm

A frequently encountered problem in signal processing field is harmonic retrieval in additive colored Gaussian or non-Gaussian noise, especially when the frequencies of the harmonic signals are closely spaced in frequency domain. The purpose of this paper is to develop novel harmonic retrieval algorithm based on blind source extraction (BSE) method from linear mixtures of harmonic signals using only one observed channel signal. First, we establish the blind source separation (BSS) based harmonic retrieval model in additive noise using the only one observed channel, at the same time, the fundamental principle of BSE based harmonics retrieval algorithm is analyzed in detail. Then, based on the established harmonic BSS model, we propose a BSE approach to the harmonic retrieval using the concept of period BSE method, as a result, the harmonic retrieval algorithm using only one channel mixed signals is derived. Simulation results show that the proposed algorithm is able to separate the harmonic source signals and yield ideal performance.     

几种基于神经网络的谐波测量方法比较研究

分析了目前已经提出的几种基于神经网络的电力谐波测量方法。从网络结构、测量原理、学习算法以及实现方法等方面进行了比较分析。结果表明：基于自适应线性神经元的谐波测量方法原理清楚、模型简单、实现方便、测量精度高，是一种很有应用前景的谐波测量方法。比较结果对于应用有指导价值。     

Finite-frequency object identification with delays

We propose a method of finite-frequency identification for stable objects with delay in the presence of an unknown external disturbance and measurement errors. This method uses a test signal which is the sum of at most as many harmonics as the number of coefficients in the object. In order to uniquely determine the delay value, we propose an approach that uses a test signal in the form of a product of a sinusoidal and exponential function. Based on this approach, we construct two identification algorithms: one for separate identification of coefficients and the delay value, another for their joint identification.