带有噪声相关性的自适应噪声抵消系统仿真研究

本文为了在语音信号处理中能消除含噪语音信号中的背景噪音,采用自适应信号处理的理论和技术来达到提高语音信号质量的目的。通过介绍自适应滤波器原理,在对自适应滤波器相关理论研究的基础上,研究了LMS自适应滤波算法,并对LMS自适应算法进行了分析。同时为了使输入的参考信号与噪声相关,加入分离周期信号与带有窄带干扰抑制的宽带信号。通过分析仿真结果表明基于LMS算法的自适应噪声抵消技术可以有效地抵消正弦干扰信号,同时加入宽带信号中的周期性噪声,在没有另外的与噪声相关的参考信号的情况下,可以使用自适应噪声抵消系统来消除这种同期性干扰噪声。     

基于提升Keystone变换的声呐宽带自适应波束形成方法

针对Keystone变换在宽带阵列预处理方面的优势和常规Keystone变换存在的阵元数据缺失问题,该文将自回归模型与常规Keystone变换相结合,提出一种基于提升Keystone变换的声呐宽带自适应波束形成算法。该算法首先将常规Keystone变换应用于宽带阵列信号的相位对齐,接着采用自回归模型对变换后各频段缺失的阵元数据进行预测补偿,最后通过稳健自适应波束形成处理获得目标方位输出结果。仿真实验结果表明,基于提升Keystone变换的宽带自适应波束形成算法性能优于常规Keystone自适应算法、指向最小方差自适应算法和聚焦自适应算法。     

一种基于矢量量化的语音信号频带扩展方法

在不增加信息量的前提下,对语音信号进行频带扩展,以提高语音的听觉质量,已经成为人们研究的热点.本文围绕语音信号频带扩展中的两个重要问题--谱包络的重建和宽带激励信号的产生,分析了传统基于矢量量化的频带扩展方法中的不足.文章中针对这两个问题,对谱包络的重建方法作了改进,并提出了一种新的宽带激励信号产生方法.实验结果均表明改进的算法明显优于传统算法.     

一种改进的语音信号非线性自适应预测编码方案

应用神经网络和Ｌｅｖｉｎｓｏｎ－Ｄｕｒｂｉｎ算法,本文提出一种改进的语音信号非线性自适应预测编码方案。用该方案实现了１６Ｋｂ／ｓ语音信号自适应预测编码器。实验结果表明,与原方案相比,本文提出的方案解码恢复后的语音质量有明显地改善。     

基于长时信息的自适应话音激活检测

语音信号的长时信息应用于话音激活检测中表现优越.利用三种听觉滤波器组,对语音信号进行非线性的谱分解,本文提出了六种基于听觉滤波器组的长时信息,并提出了基于长时信息的自适应话音激活检测算法.该算法无需训练数据,根据多种长时信息,直接在待测信号中挑选出类别明确的信号,然后利用这些信号训练分类模型,对待测信号按帧进行语音-非语音分类.在TIMIT语音库和NOISEX-92噪声库上的实验表明,该算法在极低信噪比环境下,仍表现出更高的准确性和更强的稳健性.同时,在线实验表明,算法在实时处理中仍能取得优异的性能.     

一种改进的信号灵活分割算法及语音信号清-浊音的自动分割

文中主要对王永忠等提出的灵活分割算法存在的问题做了相应的改进,并做了比较分析,然后将改进后的分割算法应用于语音信号的清-浊音自动分割中.经过大量的理论模型与实际语音信号验证该改进后的算法确实解决了二进分割算法及王永忠方法存在的问题,达到了对信号自适应有效分割.仍然采用Wesfreid等提出的清-浊音识别准则,将新的分割方法应用到实际语音信号的清-浊音自动分割中,不仅同样产生较好划分结果,而且在时间上没有过多的冗余分割.     

野战强噪声环境下的语音信号检测

本文研究了单工电台人网中的语音信号检测问题，提出了自适应预测检测方法，同时，针对自适应预测，分析了上前用比较广泛的Ｄｕｒｂｉｎ算法在语音检测中的不足，提出采用收敛快，误差小的ＬＳＬ算法     

基于卫星导航定位系统自适应抗干扰控制

在全球卫星导航系统抗干扰问题的研究中,自适应波束形成技术很好地解决了与信号不同来向的干扰的抑制问题。但对与信号同向的窄带干扰抑制程度不够,同时会滤除部分导航信号。针对以上问题,提出了一种改进的自适应波束干扰抑制算法。首先,通过级联IIR格型陷波器预测并抑制与信号同向的窄带干扰,然后,利用基于直接数据域自适应波束形成技术抑制剩余的宽带干扰。该改进算法能够有效的滤除窄带和宽带干扰,提升卫星导航系统的抗干扰性能,并在实际卫星通信应用中更具处理的实时性。最后,通过仿真实验证明了该算法的可行性。     

雷达多普勒信号数据压缩研究

在分析了多普勒信号的特性及语音信号的区别后，采用自适应离散余弦变换算法对多普勒信号进行了压缩编码，在中等编码速率下得到了较好的压缩编码效果。文中提出了相关－自适应离散余弦变换（C－ADCT）压缩编码算法，改进算法提高了自适应离散余弦变换算法的抗噪性能。     

一种基于子带GSC的语音增强算法

提出一种基于GSC的语音增强算法,该算法应用了DFT调制子带滤波器组将语音信号分解到子带进行自适应滤波,从而获得更好的增强效果以及更低的运量复杂度.同时,将范数约束自适应滤波(NCAF)算法应用于自适应噪声对消器(ANC)以降低语音的失真度.为了进一步去除增强后语音中的残留噪声,算法使用改进的Wiener后置滤波器.仿真结果表明,相对于基于全带GSC的麦克风阵列语音增强算法以及传统Wiener后置滤波算法,采用本文所用算法具有更高的输出分段信噪比.     

Zero-branch tree encoding of speech pitch/voicing signals

A computationally efficient, although suboptimal, tree encoding method for the pitch and voicing parameters of an LPC (linear predictive coding) vocoder is presented. It is shown that when pitch and voicing signals are combined, it becomes difficult to take advantage of linear predictors and at the same time avoid any errors in voicing. The modified pitch tree coder presented here solves this problem by incorporating a branch leading to zero pitch value from each node of the tree. Only two bits per analysis frame are used to convey the combined pitch/voicing signal, with 1.585 bits being used to encode nonzero pitch values     

An autocorrelation pitch detector and voicing decision withconfidence measures developed for noise-corrupted speech

The authors describe an integrated speech feature extraction method consisting of: (1) a pitch detector; (2) a voicing decision to correctly partition speech into voiced and unvoiced intervals; (3) a confidence measure which reflects the probabilistic accuracy of the voicing decision; (4) a confidence measure which reflects the expected deviation of the pitch estimate from the true pitch and the probabilistic accuracy of this deviation; and (5) smoothing techniques for the pitch detector, the voicing decision, and the two confidence measures. The focus of their research is on voiced and unvoiced speech corrupted by high levels of white noise. The voicing decision and the confidence measures are developed by observing the behavior of three features derived from the autocorrelation function and experimentally fitting curves to the data. This integrated set of algorithms is statistically analyzed for speech at seven signal-to-noise ratios     

Unvoiced/voiced classification and voiced harmonic parameters estimation using the third-order statistics

Unvoiced/voiced classification of speech is a challenging problem especially under conditions of low signal-to-noise ratio or the non-white-stationary noise environment. To solve this problem, an algorithm for speech classification, and a technique for the estimation of pairwise magnitude frequency in voiced speech are proposed. By using third order spectrum of speech signal to remove noise, in this algorithm the least spectrum difference to get refined pitch and the max harmonic number is given. And this algorithm utilizes spectral envelope to estimate signal-to-noise ratio of speech harmonics. Speech classification, voicing probability, and harmonic parameters of the voiced frame can be obtained. Simulation results indicate that the proposed algorithm, under complicated background noise, especially Gaussian noise, can effectively classify speech in high accuracy for voicing probability and the voiced parameters.     

Shape invariant time-scale and pitch modification of speech

The simplified linear model of speech production predicts that when the rate of articulation is changed, the resulting waveform takes on the appearance of the original, except for a change in the time scale. A time-scale modification system that preserves this shape-invariance property during voicing is developed. This is done using a version of the sinusoidal analysis-synthesis system that models and independently modifies the phase contributions of the vocal tract and vocal cord excitation. An important property of the system is its ability to perform time-varying rates of change. Extensions of the method are applied to fixed and time-varying pitch modification of speech. The sine-wave analysis-synthesis system also allows for shape-invariant joint time-scale and pitch modification, and allows for the adjustment of the time scale and pitch according to speech characteristics such as the degree of voicing     

A multiband excited waveform-interpolated 2.35-kbps speech codecfor bandlimited channels

Following a brief portrayal of the activities in 2.4-kbps speech coding, a wavelet-based pitch detector is invoked, which reduces the complexity of conventional autocorrelation-based pitch detectors, while ensuring smooth pitch trajectory evolution. This scheme is incorporated in a waveform-interpolated codec, which uses voiced-unvoiced (V/U) classification, and instead of simple Dirac pulses, an unconventional zinc basis function excitation is employed for modeling the voiced excitation. The required zinc-function parameters are determined in an analysis-by-synthesis loop, and for the sake of smooth waveform evolution and reduced complexity, a focused search strategy and a few further suboptimum restrictions are imposed without seriously affecting the speech quality. This baseline codec operates at a rate of 1.9 kbps, but it suffers from slight buzziness during the periods of excessive voicing. This impediment is then mitigated by invoking a mixed V/U multiband excitation, which slightly increases the bit rate to 2.35 kbps due to the transmission of the 3-b voicing strength code in each of the three excitation bands     

Frequency-domain maximum likelihood pitch determination approach

The rate of oscillation of the vocal cords known as the pitch is an important sound feature that is useful in many speech applications. A novel approach for the automatic detection and estimation of the rate of oscillation of the vocal cords is described. The importance of this approach stems from the fact that pitch determination is conducted using three independent stages: a segmentation stage; a voiced-unvoiced classification stage; and a pitch estimation stage. Segmentation and the detection of voiced segments are implemented prior to pitch estimation in order to: exclude unvoiced sounds and silence from biasing the result of pitch estimation; employ a simple segmentation procedure with low computational complexity and time-delay; enhance the accuracy of voiced-unvoiced classification by including additional features in voicing detection; help pitch tracking by testing similarities over successive segments and to make use of a different analysis domain that enables a high resolution pitch estimation. A frequency-domain maximum likelihood procedure is used for the estimation of the pitch frequency of voiced segments by maximizing a log-likelihood function over the range of possible pitch frequencies in conversational speech. An efficient simplified realization of the generalized likelihood ratio segmentation method is also presented. Computer simulations on a number of utterances show that this approach gives an accurate, reliable and robust estimation of the pitch of voiced sounds.     

Transformation of whispering voice to pseudo-real voice for unvoiced telephony and communication aid for voice-handicapped persons

For voice handicapped people, an easy to use voicing aid device is wanted. In mobile telephony, so-called non-speaking speech communication is an expected solution for essential privacy as well as for acoustic nuisance prevention. The study introduced here intends to cover both issues, introducing a system where the whispering (non-speaking voice or talk without vocal fold activation) signal is converted to pseudo-real voice signal, which is to be sent to, or heard by, the other party. The study also includes validation tests with multiple volunteers for its output legibility. Unlike general concept of speech regeneration being inclined to signal recognition or decomposition to text followed by electronic reading (voicing), our system converts it almost directly without recognition or decomposition steps. The processing is based on repetitive playback of short time autocorrelation, conducted by synthetic pitch pulse. A real time software pipeline process is under development.     

Electroglottography for Laryngeal Function Assessment and Speech Analysis

The methodology of electroglottography is briefly outlined, Major emphasis is given to validating key features of the electroglottographic (EGG) waveform using ultrahigh-speed laryngeal films. We show how the instants of glottal closure and opening may be identified from the EGG waveform. This information may be used to improve speech analysis techniques such as the pitch synchronous, closed phase, covariance analysis method. Other applications include pitch detection, the determination of intervals of voicing, unvoicing, mixed voicing and silence, improving speech synthesis, and assisting the automation of inverse filtering.     

一种特征波提取速率自适应的波形内插语音编码方案

该文提出了一种特征波形提取速率自适应于输入语音帧特性的波形内插编码方案。基于双加权长时预测增益最大原则并利用前向基音判决实现了较为可靠的基音周期估计算法,用基音周期、浊音度和波表面平坦度决定波形提取速率以及SEW(Slowly Evolving Waveform)和REW(Rapidly Evolving Waveform)的更新速率。实验证明,该文提出的波形内插(WI)编码算法相比固定波形提取速率的WI算法在平均码率和计算复杂度上均有一定程度的降低,且合成语音质量明显优于4.8kbps的CELP语音编码算法。     

基于FPGA的实时基音周期估计系统

提出一种基于现场可编程门阵列FPGA的实时基音周期估计系统。语音信号先通过模数转换器转换成无符号位的8-bit的语音数字信号,然后,对每一帧语音信号进行电平削波,并将削波后的语音信号转换为带符号位的2-bit的数字信号,再采用自相关函数方法估计语音信号的基音周期,对一帧带符号位的2-bit的数字信号做自相关运算能够转换为简单的加法运算,只要用简单的组合逻辑电路和计数器就能够实现。使用SpartanIIXC2S30芯片将实时的基音周期估计算法用芯片内的存储器、门电路和时序电路实现,达到实时基音周期估计的目的。