Speech and data communications over 942 MHz TAB and TTIB single sideband mobile radio systems incorporating feed-forward signal regeneration

The transmission of speech and data over 942 MHz pilot tone single sideband (SSB) mobile radio links is the main concern of this paper. It has been found that the use of feedforward signal regeneration enables a speech quality to be obtained in the Rayleigh fading environment which is superior to that achieved by a 25 kHz Advanced Mobile Phone Service (AMPS) type FM system and markedly superior to that obtained with a 12.5 kHz FM system. A new optimized form of SSB, phase-locked transparent tone-in-band (TTIB), is shown to be capable of achieving coherent data transmission such as M-ary phase shift keying (PSK) in the presence of Rayleigh fading without the usual "high-level" irreducible error rates. The signal processing described has wide application from line to satellite communications.     

小波包在车载移动电话语音信号提取中的应用

利用小波包理论对车载移动电话的接收信号进行小波包变换,通过设定一合适的阈值对变换后的信号进行量化处理．提取出主要由发动机产生的噪声信号,然后用实际检测信号减去小波包变换信号,得出汽车司机的语音信号．从而达到消除噪声的目的。利用MATLAB的小波工具箱对所提出的方法进行验证,结果表明提取后的信号与驾驶员的声音信号十分相似,误差较小,对发动机噪声有明显的抑制作用。     

面向旋翼飞机螺旋桨干扰的AM通信语音信号智能增强方法

为改善旋翼飞机空地语音通信质量,针对旋翼飞机螺旋桨造成的幅度调制(Amplitude Modulation,AM)信号复杂多频干扰以及恶劣机舱背景噪声,提出了一种通信语音时频掩膜智能增强方法,从而实现对机舱噪声与复杂干扰的有效抑制。该方法首先对原始时域语音信号进行分帧与加窗,通过短时傅里叶变换获取幅度谱与相位谱;然后将原始幅度谱作为网络输入,采用深度神经网络分析其语音信号的特征,采用长短期记忆网络挖掘语音信号的时序上下文信息,实现对语音时频掩膜的准确估计,并将其用于增强原始幅度谱以得到网络输出;最后结合原始相位谱,通过逆短时傅里叶变换获得增强后的时域语音信号。仿真与实际测试表明,该方法可有效抑制旋翼飞机环境下的干扰噪声,提高通信语音信号质量。     

Time-varying parametric modeling of speech

For linear predictive coding (LPC) of speech, the speech waveform is modeled as the output of an all-pole filter. The waveform is divided into many short intervals (10–30 msec) during which the speech signal is assumed to be stationary. For each interval the constant coefficients of the all-pole filter are estimated by linear prediction by minimizing a squared prediction error criterion. This paper investigates a modification of LPC, called time-varying LPC, which can be used to analyze nonstationary speech signals. In this method, each coefficient of the all-pole filter is allowed to be time-varying by assuming it is a linear combination of a set of known time functions. The coefficients of the linear combination of functions are obtained by the same least squares error technique used by the LPC. Methods are developed for measuring and assessing the performance of time-varying LPC and results are given from the time-varying LPC analysis of both synthetic and real speech.     

A Bayesian estimation approach for speech enhancement using hiddenMarkov models

A Bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed. In particular, minimum mean square error (MMSE) and maximum a posteriori (MAP) signal estimators are developed using hidden Markov models (HMMs) for the clean signal and the noise process. It is shown that the MMSE estimator comprises a weighted sum of conditional mean estimators for the composite states of the noisy signal, where the weights equal the posterior probabilities of the composite states given the noisy signal. The estimation of several spectral functionals of the clean signal such as the sample spectrum and the complex exponential of the phase is also considered. A gain-adapted MAP estimator is developed using the expectation-maximization algorithm. The theoretical performance of the MMSE estimator is discussed, and convergence of the MAP estimator is proved. Both the MMSE and MAP estimators are tested in enhancing speech signals degraded by white Gaussian noise at input signal-to-noise ratios of from 5 to 20 dB     

隐相空间的DUPSO-RPSOVF语音预测模型研究

提出了一种基于二阶Volterra级数的语音信号非线性预测模型.为克服传统的最小均方（Least Mean Square,LMS）算法在模型核系数更新时的固有缺点,引入耗散均匀搜索粒子群优化算法（Dissipative Uniform Particle Swarm Optimization,DUPSO）求解核系数,并构建了DUPSO-SOVF预测模型;为避免传统方法中相空间的重构过程,构建了隐相空间DUPSO-SOVF预测模型,在求解模型核系数时动态地求解出最优嵌入维数和延迟时间;为降低模型复杂度,在误差允许范围内进行模型关键项的提取,从而减少了核系数个数,构建了少参数的DUPSO-RPSOVF（Reduced Parameter SOVF,RPSOVF）预测模型.将英语音素、单词和短语作为实验样本数据进行仿真,结果表明：隐相空间DUPSO-SOVF模型能够准确的计算出相空间重构参数,DUPSO-SOVF和DUPSO-RPSOVF两种预测模型对单帧和多帧语音信号均具有较高的预测精度,优于PSO-SOVF和LMS-SOVF预测模型,并且能够很好地反映语音序列变化的趋势和规律,可以满足语音序列预测的要求.     

基于稀疏性的相位谱补偿语音增强算法

相位谱补偿语音增强算法通过调整相位谱对噪声进行压缩,提高重构信号的质量。针对传统的相位谱补偿(phase spectrum compensation, PSC)语音增强算法采用固定的相位补偿因子,且算法的性能易受噪声估计准确性的影响,提出了一种基于稀疏性的相位谱补偿(sparsity-based phase spectrum compensation, SPSC)语音增强算法。首先,利用噪声估计算法得到噪声幅度谱,利用基于幅度谱的语音增强算法得到目标语音幅度谱;接着,通过噪声和目标语音幅度谱之间的局部信噪比(Signal-to-Noise Ratio, SNR)来估计谱时间稀疏性;然后,利用sigmoid函数改进相位补偿因子,联合补偿因子和谱时间稀疏性,得到SPSC函数。最后,使用SPSC函数对相位谱中的谱分量进行补偿,通过短时傅里叶逆变换得到最终增强后的语音信号。仿真实验表明,在四种不同背景噪声的低信噪比下,新的相位谱补偿算法使增强语音获得了更好的LSD、PESQ和segSNR指标,说明新的算法在低信噪比下,可以有效恢复带噪语音中的语音成分,对噪声抑制效果明显,增强语音的质量和听感均有一定提升。      

Speech Enhancement Using Phase‐Dependent A Priori SNR Estimator in Log‐Mel Spectral Domain

We propose a novel phase‐based method for single‐channel speech enhancement to extract and enhance the desired signals in noisy environments by utilizing the phase information. In the method, a phase‐dependent a priori signal‐to‐noise ratio (SNR) is estimated in the log‐mel spectral domain to utilize both the magnitude and phase information of input speech signals. The phase‐dependent estimator is incorporated into the conventional magnitude‐based decision‐directed approach that recursively computes the a priori SNR from noisy speech. Additionally, we reduce the performance degradation owing to the one‐frame delay of the estimated phase‐dependent a priori SNR by using a minimum mean square error (MMSE)‐based and maximum a posteriori (MAP)‐based estimator. In our speech enhancement experiments, the proposed phase‐dependent a priori SNR estimator is shown to improve the output SNR by 2.6 dB for both the MMSE‐based and MAP‐based estimator cases as compared to a conventional magnitude‐based estimator.     

QRS feature extraction using linear prediction

This communication proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. This communication also indicates that the prediction order need not be more than two for fast arrhythmia detection. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex, the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to a set of three states pulse-code train relative to the original ECG signal. The pulse-code train has the advantage of easy implementation in digital hardware circuits to achieve automated ECG diagnosis. The algorithm performs very well in feature extraction in arrhythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contraction) detection has at least a 92 percent sensitivity for MIT/BIH arrhythmia database.     

Improved vowel onset point detection using epoch intervals

Vowel onset point (VOP) is the instant at which the onset of vowel takes place in the speech signal. Accurate detection of VOP is useful for applications such as consonant–vowel (CV) unit recognition and speech rate modification. Existing VOP detection methods determine VOPs within 40 ms deviation, which may not be suitable for the applications mentioned above. In this paper, a two level approach using multiple sources of evidence is proposed for the accurate detection of VOP. In the proposed method, at the first level, VOPs are identified by combining the complementary evidence from excitation source, spectral peaks and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in vowel region. Zero frequency filter method is used to determine the epoch locations in speech. Performance of the proposed method is analyzed using TIMIT database, and compared with the recent method which uses the combination of evidence from excitation source, spectral peaks and modulation spectrum. Using the proposed method about 85% of VOPs are detected within 10 ms deviation.     

SELP语音编码模型的抗误码改进方案

受信道误码影响时,基于SELP模型的声码器合成语音出现大量刺耳的尖锐声,严重降低语音质量。研究发现,高能量的语音信号经分析滤波器产生余量信号,能量可能明显降低,量化值较小。信道误码可能使该参数在合成端出错明显增大,再经过合成滤波器后产生尖锐声。将能量参数从原始语音信号中提取,并改进合成算法中能量参数的用法。能量较高的语音信号量化值较大,发生误码时明显增大的可能性降低,能削弱尖锐刺耳声。     

Periodic variation method for blind symbol rate estimation

In order to obtain unknown symbol rate of incoming signal at a receiver, in this paper, cyclostationary features of linear digitally modulated signals are exploited by proposed periodic variation method. A low complexity but highly accurate symbol rate estimation technique is obtained. The proposed method is based on a superposed epoch analysis over autocorrelations obtained blindly in different sampling frequencies. The obtained autocorrelations are analyzed in the frequency domain, and it is seen that there are large oscillations when the autocorrelation is obtained around the symbol rate. Then, a superposed epoch analysis is developed in order to estimate symbol rate based of the periodic variations on the frequency responses of autocorrelations. The proposed algorithm is quite accurate in the noisy environment because the noise is having no frequency component after taking Fourier transform of autocorrelations in all sampling rates, and this feature is also valid for the offset frequency that the purposed estimation is not affected by offset frequency. Thus, a successful blind symbol rate estimation algorithm is obtained, and it performs much better error performance than those using the well‐known cyclic correlation based symbol rate estimations, as it is proven by the obtained performances presented in the paper. Copyright © 2013 John Wiley & Sons, Ltd.     

对包络变化及移相误差不敏感的宽带光八步移相算法

提出了一种用于测量微观表面三维形貌的宽带光八步移相算法.该算法通过定位宽带光干涉条纹的零相位差位置实现微观轮廓的测量.计算宽带光移相干涉信号中相邻采样点的相位差得到实际移相间隔,从而实现实际移相量的在线标定以及移相误差的校正.分析了倾斜SiC平面的移相干涉条纹,计算结果的标准均方差为1.646 nm,与不存在移相误差时的计算结果吻合.宽带光八步移相算法对干涉包络的变化不敏感,能够抑制移相误差,是一种实用、高精度的微观表面轮廓测量方法.     

基于信号强度的室内定位技术

研究了基于信号强度模型的室内定位技术.通过运用线性回归、补偿式线性回归和多元回归方法,利用仿真数据建立了信号强度模型.为了理解定位误差和信号强度误差之间的关系,对这种构模方法作了分析,得出的一些重要结论有助于确定接入点(AP)的部署点及评估定位误差的范围.最后,基于IEEE802.11b MAC的典型参数进行了仿真试验.     

A Stochastic Model for a Pseudo Affine Projection Algorithm

This paper presents a statistical analysis of a Pseudo Affine Projection (PAP) algorithm, obtained from the Affine Projection algorithm (AP) for a step size alpha < 1 and a scalar error signal in the weight update. Deterministic recursive equations are derived for the mean weight and for the mean square error (MSE) for a large number of adaptive taps N compared to the order P of the algorithm. Simulations are presented which show good to excellent agreement with the theory in the transient and steady states. The PAP learning behavior is of special interest in applications where tradeoffs are necessary between convergence speed and steady-state misadjustment.     

An error-protected speech recognition system for wirelesscommunications

Future wireless multimedia terminals will have a variety of applications that require speech recognition capabilities. We consider a robust distributed speech recognition system where representative parameters of the speech signal are extracted at the wireless terminal and transmitted to a centralized automatic speech recognition (ASR) server. We propose two unequal error protection schemes for the ASR bit stream and demonstrate the satisfactory performance of these schemes for typical wireless cellular channels. In addition, a "soft-feature" error concealment strategy is introduced at the ASR server that uses "soft-outputs" from the channel decoder to compute the marginal distribution of only the reliable features during likelihood computation at the speech recognizer. This soft-feature error concealment technique reduces the ASR error rate by more than a factor of 2.5 for certain channels. Also considered is a channel decoding technique with source information that improves ASR performance     

混沌,分形理论与语音信号处理

本文旨在将新兴的混沌、分形理论引入语音信号处理。本文提出了一种新的语音信号相空间重构方法，分析、统计了语音信号最大Ｌｙａｐｕｎｏｖ指数及分维度的分布，并提出了基于分形码本的语音信号码激励线性预测编码瓣算法。本文的研究表明，混沌、分形理论在语音信号处理中有良好的应用前景。     

高精度光电编码器莫尔条纹信号质量分析方法

针对莫尔条纹信号质量对高精度编码器细分误差的影响,提出了基于离散傅里叶变换分析莫尔条纹信号质量的方法。该方法利用信号重构和傅里叶变换算法得到信号参数,真实地反应了莫尔条纹信号质量,提高了细分误差测量的准确性。编码器转动时,采集相位差为/2 的两路精码正弦光电信号,通过对采样信号的重构得到信号波形,利用离散傅里叶变换算法分析重构波形,求解信号的直流分量、幅值、相位和谐波分量等各项参数。最后,根据信号参数与细分误差的关系得到光电编码器的细分误差值,并进行了实验验证。实验结果表明,对某24 位绝对式光电轴角编码器细分误差进行测量,细分误差的峰值为+0.48和-0.21。相对于传统的细分误差测量方法,此方法测量速度快,测量精度高,适用于工作现场。     

The Effects of Equalization, Timing, and Carrier Phase on the Eye Patterns of Class-4 Partial-Response Data Signals

A general expression is derived for the eye pattern boundaries of a multilevel class-4 partial-response (PR) signal after single-sideband amplitude-modulation (SSBAM) transmission with a carrier phase error. From the expression particular results are obtained for the horizontal and vertical eye openings of the signal. Expressions are derived for the tap settings of a linear transversal equalizer using the minimum mean-square error (MMSE) criterion when the class-4 PR signal is distorted by a carrier phase error and sampled with timing error. The values of the tap settings can be used to reduce the carrier and timing phase errors. Finally, an expression for the vertical eye opening of the equalized signal is derived as a function of the carrier phase and the number of stages in the equalizer.     

基于麦克风阵列的相容时延矢量声源定位方法

时延估计是常用的声源定位方法,传统的算法将定位分为两个步骤,即先估计麦克风阵列中每一对基元的接收信号时延,然后根据这些时延用几何的方法确定声源的位置。在低信噪比下,一对麦克风的时延估计误差较大,导致定位误差较大。相容时延矢量估计算法将两步合为一步,没有逐对估计时延,而是构造一个目标函数,通过搜索得到声源的位置。仿真结果表明,在低信噪比下,只需要较短的数据,该算法仍可得到较高的定位精度。