声带麻痹植入手术的有限元仿真模拟

由于外科医生对于声带麻痹植入手术没有有效的预测,手术的失败率较高,因此采用有限元的方法进行术前模拟仿真。通过人体的喉部CT数据,使用Mimics软件提取声带与声门气管三维几何模型,然后导入到ANSYS-Fluent来模拟声带麻痹患者植入手术前后声带振动模式与喉部气流动态耦合特性,分析声门气管表面压力以及气流速度的变化情况。实验结果与临床统计数据进行分析与对比,推断运用有限元分析技术进行声带麻痹植入手术仿真模拟的研究方法合理可行,为手术方案提供了支持。     

利用目标与杂波先验知识的机载雷达发射波形自适应生成方法

发射波形自适应能够优化雷达在检测、跟踪和抗干扰等方面的能力,但其性能的好坏与雷达目标和杂波先验知识有很强的相关性。采用最大输出信杂噪比准则设计的雷达发射波形自适应方法已经在理论上被证实可行和有效。为了提升这类方法的实际应用价值,本文利用Swerling统计模型和数字高程模型（Digital elevation model,DEM）提供的目标和杂波先验信息,针对机载相控阵雷达,分析了基于最大输出信杂噪比（Signal-to-clutter-noise ratio,SCNR）准则的发射波形自适应方法性能和实用价值。仿真结果表明,这类发射波形能够自适应地随着目标和杂波的变化而改变,并实现二者的最优匹配,在提升雷达输出信杂噪比上优于传统发射波形,具有良好的实际应用前景。     

单相逆变系统建模与控制仿真研究

非线性整流性负载是影响逆变器输出电压波形质量的主要因素。针对带非线性负载的单相逆变器,其输出波形畸变较大,动态性能差和总谐波畸变率（THD）较高的缺点。为了解决上述问题,首先要解决整流性负载的模型问题。用死区模型代替整流性负载,得到具有整流性负载的单相逆变系统的仿真模型。采用了一种基于遗传算法优化的模糊控制方案,并在MATLAB上进行了仿真验证。仿真结果表明,模糊控制方法能同时实现逆变器的高精度稳态输出波形、较低的THD值和快速动态响应性能。     

基于后验概率SVM的仿真模型验证方法*

针对复杂系统建模与仿真中模型验证的特点,提出基于后验概率支持向量机（support vector machine, SVM）的仿真模型验证方法。采用基于误差分析的特征提取方法提取各可选仿真模型和实际系统输出的特征向量,利用各可选模型的特征向量及相应的模型标号组成训练集建立后验概率SVM模型,将实际系统的特征向量作用于后验概率SVM模型,依据其概率输出判别可选模型相对于实际系统的有效性。以某型飞行器制导控制系统仿真模型验证为例,证明了该方法的可行性和有效性。     

非理想Buck变换器的建模及仿真

针对以往开关变换器建模过程中一些理想化假设会引起较大误差的问题,为系统预测的稳定性,以Buck变换器为例,采用状态空间平均法建立在非理想条件下的数学模型,并利用MATLAB软件对模型进行仿真,仿真结果与理想模型和Buck变换器实际电路模型的输出波形进行对比分析,非理想模型的仿真波形和实际电路模型的波形基本一致。结论表明建立的非理想Buck变换器状态空间数学模型具有合理性和可行性,在PWM DC-DC开关变换器的建模分析中考虑寄生参数的实用性更强。     

地空信道基于OFDM/OQAM系统的波形自适应算法

传统的正交频分复用/偏移正交幅度调制（OFDM/OQAM）系统波形自适应设计主要针对具有非指数型时延功率谱和非U型多普勒功率谱的信道模型对波形进行优化,而实际中,波形自适应设计会因不同的信道模型产生不同的信道匹配准则系数。结合地空信道模型和扩展高斯函数的特性,在传统基于信干噪比SINR优化的OFDM/OQAM系统波形自适应算法的基础上提出一种新的OFDM/OQAM 系统波形自适应设计算法。该算法引入信道匹配系数β,通过信道匹配准则建立波形时频域间隔与信道最大多径时延、最大多普勒频移的关系,再结合传统SINR优化函数计算扩展因子参数,将参数反馈给发送端并调整发送端和接收端的滤波器达到波形自适应的目的。仿真结果表明,4QAM和16QAM调制下,信道匹配系数β的引入在系统误码性能上均有1.0 dB以上的改善。     

带LC滤波器的UPS三电平NPC逆变器改进模型预测控制

在UPS系统中,逆变器输出电压波形的质量决定了系统的性能,而总谐波失真(THD)是决定电压波形质量的重要因素.由于UPS逆变器输出端一般直接与负载相连,因此,在输出端加入LC滤波器可以有效改善电压波形,产生谐波失真较低的正弦电压.针对该逆变器结构,文章提出一种改进模型预测控制来解决传统有限集模型预测控制(FCS-MPC)延迟对系统的影响,利用Matlab/Simulink仿真工具,给出在线性和非线性负载下两种预测控制逆变器输出电压波形和THD仿真结果.结果表明,改进的MPC可有效提高系统性能.     

DC-DC变换电路差模传导电磁干扰预测模型研究

针对DC-DC变换电路差模传导电磁干扰信号难以提取的问题,通过分析直流人工电源网络(DC-LISN,DC line impedance stabilization network)的网络特性,建立基于PSpice的差模传导干扰预测模型.该模型采用的DC-LISN可实现直流信号的正向传输、高频干扰信号在测试段无衰减提取的功能.通过PSpice仿真和实验分别提取了电源侧差模传导干扰的时域波形并进行频谱分析,结果表明,基于DC-LISN的PSpice差模传导干扰预测模型与实际测试结果在幅值和频谱成分方面吻合较好,从而证明了该模型对预测实际变换器产生的差模干扰是有效的.     

基于DSPBuilder的脉冲治疗仪的波形设计

脉冲治疗仪采用不同波形的电脉冲实现对相应疾病的治疗,利用DSPBuilder建立数学模型,然后用SignalCompiler工具对其进行编译,产生VHDL源程序,经过模拟仿真后,下载到FPGA芯片中,可以实现各种脉冲波形的输出。给出了6种基本波形及调制波形的设计方法,并采用Modelsim进行了仿真。采用此方法具有设计灵活、调节方便、输出信号稳定等优点,现场可编程使产品能够更方便地实现优化升级。     

小型Tesla变压器线圈参数仿真及应用

为了提高变压器的研制效率,方便其工作状态的分析,运用ANSYS软件进行小型Tesla变压器的建模仿真.将仿真计算数据与相应的实验测量结果进行对比分析,验证仿真所得的电感、电容等参数的可靠性,并在仿真计算的基础上,根据Tesla变压器的数学模型计算出变压器输出电压波形,与实际测量的结果相比较,基本吻合,实现了变压器由结构模型到输出波形的模拟计算.     

Production of Various Vocal Cord Vibrations Using a Mechanical Model for an Anthropomorphic Talking Robot

We developed a three-dimensional mechanical vocal cord model for Waseda Talker No. 7 (WT-7), an anthropomorphic talking robot, for generating speech sounds with various voice qualities. The vocal cord model is a cover model that has two thin folds made of thermoplastic material. The model self-oscillates by airflow exhausted from the lung model and generates the glottal sound source, which is fed into the vocal tract for generating the speech sound. Using the vocal cord model, breathy and creaky voices, as well as the modal (normal) voice, were produced in a manner similar to the human laryngeal control. The breathy voice is characterized by a noisy component mixed with the periodic glottal sound source and the creaky voice is characterized by an extremely low-pitch vibration. The breathy voice was produced by adjusting the glottal opening and generating the turbulence noise by the airflow just above the glottis. The creaky voice was produced by adjusting the vocal cord tension, the sub-glottal pressure and the vibration mass so as to generate a double-pitch vibration with a long pitch interval. The vocal cord model used to produce these voice qualities was evaluated in terms of the vibration pattern as measured by a high-speed camera, the glottal airflow and the acoustic characteristics of the glottal sound source, as compared to the data for a human.     

Robust glottal source estimation based on joint source-filter model optimization

This paper describes a robust glottal source estimation method based on a joint source-filter separation technique. In this method, the Liljencrants-Fant (LF) model, which models the glottal flow derivative, is integrated into a time-varying ARX speech production model. These two models are estimated in a joint optimization procedure, in which a Kalman filtering process is embedded for adaptively identifying the vocal tract parameters. Since the formulated joint estimation problem is a multiparameter nonlinear optimization procedure, we separate the optimization procedure into two passes. The first pass initializes the glottal source and vocal tract models by solving a quasi-convex approximate optimization problem. Having robust initial values, the joint estimation procedure determines the accuracy of model estimation implemented with a trust-region descent optimization algorithm. Experiments with synthetic and real voice signals show that the proposed method is a robust glottal source parameter estimation method with a high degree of accuracy.     

Analysis of nonlinear dynamics of vocal folds using high-speed video observation and biomechanical modeling

Primary voice production occurs in the larynx through vibrational movements carried out by vocal folds. However, many problems can affect this complex system resulting in voice disorders. In this context, time–frequency–shape analysis based on embedding phase space plots and nonlinear dynamics methods have been used to evaluate the vocal fold dynamics during phonation. For this purpose, the present work used high-speed video to record the vocal fold movements of three subjects and extract the glottal area time series using an image segmentation algorithm. This signal is used for an optimization method which combines genetic algorithms and a quasi-Newton method to optimize the parameters of a biomechanical model of vocal folds based on lumped elements (masses, springs and dampers). After optimization, this model is capable of simulating the dynamics of recorded vocal folds and their glottal pulse. Bifurcation diagrams and phase space analysis were used to evaluate the behavior of this deterministic system in different circumstances. The results showed that this methodology can be used to extract some physiological parameters of vocal folds and reproduce some complex behaviors of these structures contributing to the scientific and clinical evaluation of voice production.     

Mechanical construction of a human vocal system for singing voice production

Various ways of vocal sound production are being actively studied. We are constructing a phonetic machine with a vocal chord and a vocal tract based on mechatronics technology. Mechanical construction of a human vocal system is considered to generate natural voice so that it can be advantageously applied to singing voice production. In voice generation, analysis and mechanical realization of the behaviors of the vocal chords and vocal tract are required. Furthermore, the fluid mechanical system is less stable, thus making control more difficult. Several motors are employed to manipulate the mechanical vocal system. Mappings between motor positions and the produced vocal sounds are automatically established in the learning phase. In the singing performance, the system is able to sing while vocal pitches and phonemes are adaptively controlled by an auditory feedback process. This paper presents the latest mechanisms of our mechanical vocal system together with adaptive tuning algorithms of the physical mechanism with an auditory system.     

Applied aspects of the synthesis and analysis of voice information

The authors present new results in solving problems of concatenative segment synthesis of voice information with prosody and vocal utterance, computer modeling of human voice signals based on joint models of human voice source and vocal tract, and speech signal preprocessing for automated documenting systems. The experiments show the efficiency of the proposed approaches.     

基于PSO优化GRNN的语音转换方法

提出了一种基于粒子群算法PSO优化广义回归神经网络GRNN模型的语音转换方法。首先,该方法利用训练语音的声道和激励源的个性化特征参数分别训练两个GRNN,得到GRNN的结构参数;然后,利用PSO对GRNN的结构参数进行优化,减少人为因素对转换结果的影响;最后,对语音的韵律特征、基音轮廓和能量分别进行了线性转换,使得转换后的语音包含更多源语音的个性化特征信息。主客观实验结果表明:与径向基神经网络RBF和GRNN相比,使用本文提出的转换模型获得的转换语音的自然度和似然度都得到了很大的提升,谱失真率明显降低并且更接近于目标语音。     

Voice conversion using General Regression Neural Network

The objective of voice conversion system is to formulate the mapping function which can transform the source speaker characteristics to that of the target speaker. In this paper, we propose the General Regression Neural Network (GRNN) based model for voice conversion. It is a single pass learning network that makes the training procedure fast and comparatively less time consuming. The proposed system uses the shape of the vocal tract, the shape of the glottal pulse (excitation signal) and long term prosodic features to carry out the voice conversion task. In this paper, the shape of the vocal tract and the shape of source excitation of a particular speaker are represented using Line Spectral Frequencies (LSFs) and Linear Prediction (LP) residual respectively. GRNN is used to obtain the mapping function between the source and target speakers. The direct transformation of the time domain residual using Artificial Neural Network (ANN) causes phase change and generates artifacts in consecutive frames. In order to alleviate it, wavelet packet decomposed coefficients are used to characterize the excitation of the speech signal. The long term prosodic parameters namely, pitch contour (intonation) and the energy profile of the test signal are also modified in relation to that of the target (desired) speaker using the baseline method. The relative performances of the proposed model are compared to voice conversion system based on the state of the art RBF and GMM models using objective and subjective evaluation measures. The evaluation measures show that the proposed GRNN based voice conversion system performs slightly better than the state of the art models.     

一种基于码书映射的高效语音转换方法

为了使机器人在人一机语音交互过程中更为自然,利用语音转换技术改变源语音个性特征（机械音）．进而变化为自然的目标人语音,是一种可行的方案。然而,当前的语音转换主流方法在实时性要求高且内核小的嵌入式机器人中并不适用。引入一种高效的改进型码书转换方法。该方法首先通过匹配线性谱频率参数的相对距离来求取加权系数,进而实现码字的预测重构;其次．对预测的码字进行带宽修正。克服频谱偏移问题。实验结果表明：该方法相比较传统方法,在转换性能相当的条件下,运行时间缩短75％左右。     

Transforming Perceived Vocal Effort and Breathiness Using Adaptive Pre-Emphasis Linear Prediction

This paper presents a technique to transform high-effort voices into breathy voices using adaptive pre-emphasis linear prediction (APLP). The primary benefit of this technique is that it estimates a spectral emphasis filter that can be used to manipulate the perceived vocal effort. The other benefit of APLP is that it estimates a formant filter that is more consistent across varying voice qualities. This paper describes how constant pre-emphasis linear prediction (LP) estimates a voice source with a constant spectral envelope even though the spectral envelope of the true voice source varies over time. A listening experiment demonstrates how differences in vocal effort and breathiness are audible in the formant filter estimated by constant pre-emphasis LP. APLP is presented as a technique to estimate a spectral emphasis filter that captures the combined influence of the glottal source and the vocal tract upon the spectral envelope of the voice. A final listening experiment demonstrates how APLP can be used to effectively transform high-effort voices into breathy voices. The techniques presented here are relevant to researchers in voice conversion, voice quality, singing, and emotion.     

A hybrid expert system approach for telemonitoring of vocal fold pathology

Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology.