基于约束型卡尔曼滤波的最大似然无失真波束形成器

自适应波束形成技术可以有效地拾取高质量的语音信号。近期提出的最大似然无失真（Maximum Likelihood Distortionless Response, MLDR）波束形成器不需要盲估计噪声协方差矩阵,具有很好的应用前景。本文提出了一种基于约束型卡尔曼滤波的MLDR波束形成器并给出了其低复杂度实现算法。将MLDR波束形成器设为卡尔曼滤波器的内部状态变量,采用一阶马尔科夫过程对其建模,而卡尔曼滤波器的观测方程则由MLDR波束形成器的代价函数构成。此外,通过对角化近似,进一步地降低了波束形成器的计算复杂度。在CHiME-3数据集上的测试结果表明,所提的对角化近似的波束形成器在计算复杂度更低的情况下取得了和已有在线实现的MLDR波束形成器相近的性能。      

改进的后滤波波束形成器语音增强算法

该文提出了一种具有后滤波的波束形成器的语音增强改进算法。该算法主要解决维纳滤波器的理想信号功率谱估计,结合自功率谱减法和互功率谱减法计算出尽可能多的功率谱估计值,以使平均结果更接近于真实值,同时修正了声源移动引起的互功率谱变化。实验结果信噪比提高5dB以上,汽车环境中基于隐含马尔可夫模型(HMM)的小词汇量短语识别达到84％。从信噪比、平均谱距离和语音识别率可以看出该算法有效去除了原始算法中易残留的低频噪声,减少了语音信号失真。     

语音信号去混响原理与技术

语音信号去混响技术在通信、语言识别等方面有重要应用。介绍了国内外相关研究动态和方法,阐述了声音混响过程和倒谱法去混响原理,简要介绍了传声器阵列-倒谱法去混响技术。     

小波变换去噪法在语音增强中的应用

本文论述了小波变换去噪原理和几种典型的语音去噪方法及语音增强的实现过程.     

多通道CDR线性预测语音去混响方法

针对封闭环境下的混响对智能语音处理系统性能的影响,论文提出了一种麦克风阵列语音去混响方法.考虑到阵列接收信号所构建的协方差矩阵存在部分数值为零,进而造成输出语音信号失真的情况,论文给出基于两个通道语音信号功率谱的相干扩散功率比(CDR)来实现协方差初始化,采用递归平均得到阵列接收信号协方差矩阵的估计值,进行通过多通道线...     

一种改进的子波域语音去噪方法

分析了信号和噪声在子波域的不同特征表现,提出了一种改进的子波域语音去噪方法,该方法用一个随尺度变化的门限阈值来抑止带噪音信号在不同尺度上噪声的子波系统,从而更好地实现在重构语音信号中消除噪声的目的。     

远场-近场折中的波束形成器设计及SOPC实现

针对语音信号处理中传统的固定波束形成器,需要给定远场声源或近场声源,导致应用场合受到局限的问题,提出了用最小平方方法实现远场-近场折中的固定波束形成器设计,给出了系统在SOPC上的实现过程,验证了该方案具有运算简单,应用广泛而且失真度小的特性。     

波束域最大似然测高方法

针对雷达在低仰角搜索和跟踪目标时波束打地、受阵地反射多径影响严重的问题,提出了波束域最大似然雷达低仰角测高方法。该方法首先形成俯仰多波束覆盖目标,然后使用最大似然算法对多波束的目标数据进行处理,通过空间谱能量最大化的角度信息估计目标仰角和高度。与传统的和差波束及多波束比幅测角方法相比,该方法受多径影响小,具有较高的角度分辨率和高测量精度。计算机仿真和实测数据的处理结果验证了该算法可行有效。     

基于区域约束的双耳近场自适应波束形成算法

提出了一种基于区域约束的双耳近场自适应波束形成算法，该算法将目标信号及其附近区域内的若干个导向矢量组成的矩阵进行特征值分解，并在最小化输出信号能量的同时对主要的特征向量进行约束，从而有效解决传统近场波束形成器对目标声源的方位估计误差和位置扰动过于敏感的问题。实验结果表明，所提方法可以有效抑制远场同向干扰声源，并且相对于传统近场波束形成器具有更高的鲁棒性。客观评价指标表明，该算法的降噪性能及语音质量提升方面均优于对比算法。      

基于近场波束形成的麦克风阵列语音增强方法

当麦克风阵列用于封闭环境中非手持式语音拾取时,必须面对的一个问题是声场为阵列近场的问题。该文在子带自适应波束形成方法的基础上,引进了一种基于近场波束形成的麦克风阵列语音增强方法。该方法充分利用了近场球面波的波前弯曲率,有效地衰减了混响和噪声对期望信号的影响。仿真实验结果表明,在小房间混响条件下,基于近场波束形成的麦克风阵列语音增强方法取得了较好的噪声抑制效果。     

Maximum Likelihood Stacking in White Gaussian Noise with Unknown Variances

In seismic exploration, the process of estimating the source produced signal common to a collection of sampled data recordings contaminated with additive noise is known as stacking. Stacking is used when a low energy acoustic source can be repeated in the same physical location (or nearly so) and a collection of data recordings are gathered at the same electromechanical transducer from the multiple source initiations. This stacking process is primarily aimed at reducing environmental noise which frequently dominates each individual recording when low energy sources are used. This is in contrast to other techniques used to reduce or eliminate so called source generated noise which behaves as signal in the stacking process. Specifically, the problem we address is the simultaneous maximum likelihood (ML) estimation of signal values and unknown noise distribution parameters from a collection of sampled data recordings, each containing the same signal sequence. In this paper we restrict our consideration to independent, zero mean, Gaussian noise variates with unknown variance which may differ between data records. This model is justified when a broadband analog noise source is subsequently low pass filtered along with the signal and the result sampled at the Nyquist rate. The linear filtering tends to produce a Gaussian noise process and the Nyquist rate sampling produces approximately uncorrelated samples. The ML estimates of signal values and noise variances are obtained numerically by steepest ascent.     

Maximum Likelihood Unsupervised Source Separation in Gaussian Noise

This paper presents a new Maximum Likelihood (ML) based approach to the separation of convolutive mixtures of unobserved sources in the presence of Additive Gaussian Noise (AGN). The proposed method proceeds in two steps. First, the mixing system coefficients are estimated in the ML sense and, afterwards, this information is employed to attain source separation according to either the ML or the linear Minimum Mean Square Error (MMSE) criteria. System coefficient estimation is carried out in a block-iterative way using an extension of the Expectation Maximization (EM) method. Both deterministic and stochastic (Monte Carlo) implementations of the resulting estimation algorithm are considered. The proposed algorithms rely on the knowledge of the sources joint probability density function (p.d.f.). This is a fairly realistic assumption in applications such as digital communications but computer simulations reveal that it is not an stringent requirement. The proposed estimation algorithm can be successfully used with a tentative p.d.f. when this is not known a priori.     

Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification

In this paper we review some of the ways in which context has been handled in the remote-sensing literature, and we introduce additional possibilities. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.     

Generalized Maximum Likelihood Noncoherent Block Detection for Decode-and-Forward Relay Systems

This paper considers noncoherent cooperative decode-and-forward （DF） half- duplex multi-branch relay systems. Each relay branch is modeled as a probabilistic transition system at the last hop, and thus it can be considered as a relaying chain comprising multi-hop relays. An approximation to the generalized maximum likelihood （ML） noncoherent block detection is derived for uncoded M-ary modulation in a faded noisy environment. In particular, the derived noncoherent block detection in a noiseless case is equivalent to a multichannel reception with full diversity. Furthermore, the generalized detection is extended specifically to block coded M-ary phase shift keying （MPSK） modulation. For a DF three node relay system using block coded quadrature phase shift keying （QPSK）, simulation results are provided to examine the end-to-end error performance of the noncoherent detection with considering the effects of network geometry and power allocation, respectively. It is shown that under a fixed power allocation, a proper relay placement can yield near full diversity for large signal-to-noise ratio.     

相干信源波达方向估计的广义最大似然算法

论文基于广义导向矢量和广义阵列流形矩阵,建立了多相干源(组)情况下的阵列数据模型,然后提出了波达方向估计的广义最大似然算法。对于广义最大似然算法,入射信源可以是多相干源(组),阵列的几何结构也没有任何约束,而且它分辨的信源数还可以大于阵元数。随后,论文将广义最大似然算法与常规最大似然算法进行了理论比较,并给出了广义最大似然竹法方位估计一致性的证明和方位估计方差的计算公式。理论分析表明,在空间只存在非相干信源时,广义最大似然算法与常规的最大似然算法是等价的,而在空间存在多相干源(组)时,它的性能较常规最大似然算法有较大的改进,方位估计的方差更小。最后论文利用遗传算法实现了广义最大似然算法,并通过MonteCarlo仿真实验证明了广义最大似然算法的有效性。     

TURBO EQUALIZATION WITH JOINTLY GAUSSIAN EQUALIZER

A Jointly Gaussian (JG) equalizer is derived for turbo equalization based on an augmented real matrix representation of channel model and a Gaussian approximation of the received symbol sequence. Using matrix inversion lemma and Cholesky decomposition, a lowcomplexity implementation of JG equalizer is also presented. The simulation results and complexity comparison confirm that turbo equalization with JG equalizer has a better performance and a lower complexity than the existing turbo equalization with linear minimum mean squared error equalizer.     

Generalized IRA Erasure Correcting Codes for Hybrid Iterative/Maximum Likelihood Decoding

The design of low-density parity-check (LDPC) codes under hybrid iterative / maximum likelihood decoding is addressed for the binary erasure channel (BEC). Specifically, we focus on generalized irregular repeat-accumulate (GeIRA) codes, which offer both efficient encoding and design flexibility. We show that properly designed GeIRA codes tightly approach the performance of an ideal maximum distance separable (MDS) code, even for short block sizes. For example, our (2048,1024) code reaches a codeword error rate of 10^-5 at channel erasure probability isin= 0.450, where an ideal (2048,1024) MDS code would reach the same error rate at isin = 0.453.     

复数域网络编码中最大似然解码算法的改进

针对复数域网络编码中继网络,提出一种改进的中继处的最大似然解码算法。当传统的最大似然解码算法使用在复数域网络编码中继网络中时,计算复杂度会随着信息星座图映射方式的改变而大大增加。为了降低计算复杂度,在前人研究的基础上提出一种改进的最大似然解码算法,该算法利用无线中继网络通信系统的非对称性,在解码时,先进行预判决,然后进行精确判决,这样可以大大降低计算复杂度,提高了系统性能。     

Maximum Likelihood Estimation For The 2-Parameter Weibull Distribution Based On Interval-Data

MLE techniques are presented for estimating time-to-failure distributions from interval-data. Interval-data consist of adjacent inspection times that surround an unknown failure time. Censored interval-data bound the unknown failure time with only a lower time. The 2-parameter Weibull distribution is examined as the failure distribution. Parameter estimates from interval-data and from the midpoints of the intervals are compared for 6 shapes of the Weibull distribution. The results from Monte Carlo simulation runs are used to examine the s-bias and S-variability of the parameter estimates.