面向二元麦克风小阵列改进的广义旁瓣抵消器语音增强算法

二元麦克风小阵列在手机、助听器等受空间、成本以及运算能力限制的设备中被广泛研究用以提高目标语音质量。二元麦克风小阵列中语音增强算法主要包括波束形成方法以及相干性滤波器方法。波束形成方法的思想是利用目标声源相对阵列的位置关系获取相应的时域和空域信息,可以保留目标声源方向的信号而抑制其他方向的干扰信号;相干性滤波器方法则通过阵元间不同信号的相关性进行噪音抑制。考虑这两种类型方法的优点,本文提出一种面向二元麦克风小阵列改进的广义旁瓣抵消器语音增强算法,通过在广义旁瓣抵消器的固定波束形成支路上使用相干性滤波器,提高固定波束形成输出信号的信噪比,然后在广义旁瓣抵消器自适应支路利用阵列的时域和空域信息对固定波束形成支路输出的信号中残余噪音进行估计,进而获得增强后目标输出信号。仿真和实际试验表明,本文提出的算法明显优于单独使用小阵列波束形成算法和相干性滤波器算法。      

Analysis of the power spectral deviation of the general transfer function GSC

In recent work, we considered a microphone array located in a reverberated room, where general transfer functions (TFs) relate the source signal and the microphones, for enhancing a speech signal contaminated by interference. It was shown that it is sufficient to use the ratio between the different TFs rather than the TFs themselves in order to implement the suggested algorithm. An unbiased estimate of the TFs ratios was obtained by exploiting the nonstationarity of the speech signal. In this correspondence, we present an analysis of a distortion indicator, namely power spectral density (PSD) deviation, imposed on the desired signal by our newly suggested transfer function generalized sidelobe canceller (TF-GSC) algorithm. It is well known that for speech signals, PSD deviation between the reconstructed signal and the original one is the main contribution for speech quality degradation. As we are mainly dealing with speech signals, we analyze the PSD deviation rather than the regular waveform distortion. The resulting expression depends on the TFs involved, the noise field, and the quality of estimation of the TF's ratios. For the latter dependency, we provide an approximated analysis of estimation procedure that is based on the signal's nanstationarity and explore its dependency on the actual speech signal and on the signal-to-noise ratio (SNR) level. The theoretical expression is then used to establish empirical evaluation of the PSD deviation for several TFs of interest, various noise fields, and a wide range of SNR levels. It is shown that only a minor amount of PSD deviation is imposed on the beamformer output. The analysis presented in this correspondence is in good agreement with the actual performance presented in the former TF-GSC paper.     

宽带恒定束宽盲波束形成算法

针对传统盲源分离算法对宽带阵列信号适用性较差的问题,提出一种基于时频分析的宽带恒定束宽盲波束形成算法。该算法首先将接收信号变换到时频域上并提取出单源点。然后,对单源点聚类并求解信号在不同频点上的导向矢量。最后,通过提出一种信号来向未知的空间响应变化约束方法,实现宽带恒定束宽盲波束形成。该算法避免了将宽带盲波束形成转换为卷积混合的盲源分离,因而不存在时域盲源分离算法中系统参数随滤波器阶数急剧增加的问题,也不存在频域算法中排序和幅度模糊的问题。仿真结果表明,算法能够较好地实现宽带信号的盲分离,且输出信干噪比高于时域、频域以及时频域盲源分离算法,实测数据的处理结果验证了该算法的实用性。      

基于相干性滤波器的广义旁瓣抵消器麦克风小阵列语音增强方法

为了克服传统麦克风小阵列语音增强算法噪音抑制能力有限的问题,该文提出一种基于相干性滤波器的广义旁瓣抵消器语音增强算法, 该算法基于动态平滑系数噪声谱估计来获得相干性滤波器,分别对每个阵元接收到的信号进行滤波用以抑制包括混响等噪声信号的干扰,并把滤波后的信号作为输入信号,使用基于小阵列的广义旁瓣抵消器波束形成算法抑制残余噪声信号的干扰。模拟和实际试验表明,该文提出的算法明显优于单独使用小阵列波束形成算法和相干性滤波器算法。     

利用相位时频掩蔽的麦克风阵列噪声消除方法

本文提出了一种在干扰声源和背景噪声存在条件下麦克风阵列噪声消除的方法。麦克风阵列通过波束形成增强由导向矢量所指定方向的目标声源来抑制背景噪声。然而,现有的波束形成算法在干扰声源存在的情况下,无法进行准确的导向矢量估计。为此,本文提出一种基于音频信号互相关功率谱相位的麦克风阵列噪声消除方法。首先通过音频信号的相位时频掩码估计导向矢量,并对其进行波束形成,从而有效抑制干扰声源和背景噪声;然后利用语音存在概率,采用最大似然的方法估计波束形成后信号中残留的干扰噪声功率谱密度,对其进行后处理,进一步抑制残留干扰和噪声。实验结果表明在干扰声源和背景噪声存在的条件下,所提方法有效地实现了麦克风阵列噪声消除,且各种性能指标优于基线方法。      

基于多级阻塞的稳健相干自适应波束形成

针对期望信号波达角(DOA)估计误差较大时相干波束形成性能下降的问题,该文提出一种基于多级阻塞的稳健相干自适应波束形成算法。该算法首先定义阻塞矩阵,推导多级阻塞原理,并利用其滤除阵列接收信号中的期望信号;然后给出空间中只存在期望信号时,子阵与全阵间阵列流型的映射关系,据此推导全阵扩展变换,并证明其在干扰信号存在条件下的有效性;最终利用扩展变换获取全阵最优权矢量,实现相干波束形成。该算法对期望信号波达角估计误差稳健,且无需干扰信号来向的先验信息,同时可以有效避免阵列孔径的损失。仿真分析验证了算法的优越性和理论分析的有效性。     

一种基于互相关神经网络的声呐盲波束形成方法研究

针对水声环境和水声信号的特点，提出了一种基于神经网络的声呐盲波束形成算法。该方法利用水声信号的循环平稳特性把波束形成权向量的求解问题转化为阵列接收信号互相关函数的奇异值分解问题；引入一种互相关神经网络求解阵列接收信号相关函数的奇异值，从而减小了运算的代价，可高效实现盲波束形成。提出的改进互耦Hebbian学习规则有效地提高了神经网络权值的更新速度，为问题的实时求解提供了有效的途径。该方法还能抑制噪声和干扰的影响，表现出较强的顽健性。仿真实验验证了算法的正确性。     

The performance of the least mean squares algorithm combined withspatial smoothing

Many practical signal environments involve correlation between desired and undesired signals, causing narrowband adaptive array beamformers to exhibit signal cancellation. Spatial smoothing is a technique that can perform beamforming in such environments. This method can be incorporated into an adaptive algorithm, such as least mean squares (LMS), possibly altering the well-known performance characteristics of the algorithm. We discuss methods for combining spatial smoothing with the LMS algorithm in an array with a generalized side-lobe canceler (GSC) structure. The first of these methods is an electronic version of mechanically dithering the array. We show that this well-known method obeys a set of nonhomogeneous dynamical equations, resulting in a limit cycle that increases the misadjustment of the algorithm. The previously reported parallel spatial processing algorithm is also shown to have this increased misadjustment. We then introduce two methods that do not suffer from this misadjustment increase. We compare the methods' computational complexity and performance, in terms of stability and steady-state behavior, including weight misadjustment, GSC output power, and signal-to-noise ratio (SNR). In conclusion, we find that the limit cycle of the first method can be avoided without any increase in complexity by using one of the new methods     

一种近场环境下的麦克风阵列语音增强方法

针对语音源位于阵列近场而干扰噪声源位于阵列远场的声学环境,本文提出了一种基于近场双自适应波束形成的麦克风阵列语音增强方法。该方法利用近场声波波前的特点,主通道采用最小方差无失真响应准则的近场优化波束形成器,辅助通道采用双自适应波束形成技术,从而有效地抑制了混响和噪声对语音信号的影响。仿真实验结果表明,在房间混响条件下,本文方法具有良好的噪声抑制性能。     

拥挤干扰环境下的一种多载波扩频信号接收波束成形方法

针对拥挤干扰环境中的多载波扩频通信系统,提出了一种基于期望信号波达方向估计和干扰功率空间谱分析的波束成形方法。该方法首先利用多载波扩频调制信号的频率分集特性估计期望信号的波达方向,然后利用空间谱分析估计干扰信号的空间功率分布,在此基础上完成波束成形。该方法能够在拥挤强干扰环境下精确估计信号的波达方向,从而保证了干扰信号空间谱估计的有效性。仿真结果表明,即使干扰信号的功率显著强于期望信号,本算法仍然能够精确地估计出期望信号的波达方向,从而保证算法的有效性。     

基于权值约束变换误差的虚拟阵列波束形成

针对内插变换应用于波束形成时,适用角度范围受限的问题,提出了一种利用权值约束变换误差的改进算法.该方法将权值和变换误差约束为近似正交的关系,在虚拟阵列接收信号的协方差矩阵上加载变换误差协方差矩阵,改善了变换误差过大时波束形成性能下降的问题.仿真结果表明,改进后的算法应用于大角度内插变换波束形成时,在干扰处仍能形成稳定的较深的零点,增加了虚拟阵列波束形成的输出信干噪比（Signal to Interference and Noise Ratio,SINR）.与现有方法相比,该方法增加了内插变换应用于虚拟阵列波束形成时的适用角度范围,且能够补偿阵列本身带来的误差,计算复杂度较低.     

Coherent interference suppression with complementally transformedadaptive beamformer

This paper proposes a beamforming scheme for suppressing coherent interference with an array of arbitrary geometry. The scheme first uses estimates of the source directions to construct a transformation, which removes the desired signal while retaining the coherent interference. Optimum beamforming is then performed on the transformed data containing only interference and noise to produce the maximum output signal-to-interference-plus-noise ratio (SINR). Analysis and numerical results demonstrate that the proposed complementally transformed beamformer significantly outperforms the conventional multiply constrained minimum variance (MCMV) beamformers     

利用信号功率谱密度的麦克风阵列噪声消除方法

本文研究了一种在背景噪声和干扰噪声存在的情况下基于麦克风阵列的噪声消除方法,具有准确的指向性。波束形成可以更好的获取指定方向的增强语音及抑制其它方向的噪声的效果。而现已存在的波束形成的方法处理后,增强之后的语音仍然会存在部分的干扰噪声。针对这样的问题,本文提出了一种利用信号功率谱密度比值的广义旁瓣消除波束形成方法来进一步实现对背景噪声和干扰噪声的抑制。此外,本文还进一步利用深度神经网络的方法,通过训练多目标函数下的掩蔽值结合最优改进对数谱幅度,做后置滤波可以更高效地对残留干扰噪声进行消除。本文中通过对比实验,比较了不同的基线方法,更好地验证了所提出算法的有效性。      

Underdetermined blind separation of non-disjoint signals in time-frequency domain based on matrix diagonalization

To estimate precisely the mixing matrix and extract the source signals in underdetermined case is a challenging problem, especially when the source signals are non-disjointed in time-frequency (TF) domain. The conventional algorithms such as subspace-based achieve blind source separation exploiting the sparsity of the original signals and the mixtures must satisfy the assumption that the number of sources that contribute their energy at any TF point is strictly less than that of sensors. This paper proposes a new method considering the uncorrelated property of the sources in the practical field which relaxes the sparsity condition of sources in TF domain. The method shows that the number of the sources that exist in any TF neighborhood simultaneously equals to that of sensors. We can identify the active sources and estimate their corresponding TF values in any TF neighborhood by matrix diagonalization. Moreover, this paper proposes a method for estimating the mixing matrix by classifying the eigenvectors corresponded to the single source TF neighborhoods. The simulation results show the proposed algorithm separates the sources with higher signal-to-interference ratio compared to other conventional algorithms.     

存在相干信号时的最优波束形成

本文提出一种新的存在相干信号时的最优波束形成方法。该方法首先利用估计得到的期望信号和相干信号 的方向形成变换矩阵,去掉数据中的期望信号和相干信号成分，求得不相关干扰信号的子空间以及其正交子空间，然后得到期望信号和相干信号的合成导向矢量在该正交子空间中的投影矢量，并把该投影矢量作为自适应权矢量。经理论分析表明，这种方法基本上和理论上的最优方法相同。另外，该方法可以适用于任意的阵列结构，并且对期望信号和相干信号方向估计误差具有很强的稳健性。计算机仿真结果证实了本文方法的有效性。     

A Robust Space-Time Generalized Sidelobe Canceller

This paper deals with adaptive array beamforming based on a two-dimensional generalized sidelobe canceller (GSC) with robust capability. It has been known that the performance of conventional GSC is quite sensitive even to pointing error. In conjunction with the joint desired user’s code-aid and signal subspace estimating approach, the proper quiescent weight vector and blocking matrix of GSC can be obtained to suppress the leakage of desired signal. However, space signature is estimated by exploiting the knowledge of the spreading code of the user of interest and the orthogonality between noise and signal subspaces. In this paper, the space-time signature estimation can be integrated jointly. Therefore, the desired signal cancellation does not occur; even the pointing error is relatively large. Computer simulation results show the effectiveness of the proposed approach.     

Two-Dimensional Adaptive Array Beamforming With Multiple Beam Constraints Using a Generalized Sidelobe Canceller

This paper deals with the problem of two-dimensional (2-D) adaptive array beamforming with multiple beam constraints (MBC) using a generalized sidelobe canceller (GSC). We present a method for the construction of signal blocking matrix required by the 2-D GSC. The resulting 2-D adaptive beamformer can provide almost the same performance as conventional 2-D adaptive beamformers based on a linearly constrained minimum variance (LCMV) criterion. The effectiveness of the proposed GSC is that the construction of the required signal blocking matrix requires only the computation of a few entries from analytical formulas. In comparison with conventional methods, the proposed technique gets rid of the computational complexity due to the eigendecomposition required for finding the 2-D signal blocking matrix. For dealing with the performance degradation due to coherent interference, we present a 2-D weighted spatial smoothing scheme to effectively alleviate the coherent jamming effect. Several simulation examples are provided for illustration and comparison.     

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。      

CDMA系统智能天线盲自适应波束形成

给出了基于最大化信号对干扰和噪声的功率比(MSINR)准则的盲自适应波束形成在线递推算法,旨在提高DS-CDMA系统中智能天线的性能.将MSINR准则转化为一种新的等价的无约束准则函数,通过最大化此准则函数来在线估计最优波束形成向量.算法直接利用了DS-CDMA系统解扩前和解扩后的观测信号,无须训练序列.通过在线迭代估计,降低了计算波束权值的复杂度.不同通信环境中的仿真结果表明,该算法具有较高的估计精度和很好的抗多址干扰能力.     

基于二阶统计量的盲波束形成方法

为了恢复二阶统计独立的源信号,本文给出一种基于二阶统计量的盲波束形成方法。这个方法是基于二阶统计独立的源信号之间统计互不相关的特性．此方法适用于任意流形阵列 的盲波束形成．除了要求噪声是白色的外,源信号必须是有色的随机信号或确定信号。没有必要估计波达方向．在源信号估计中,此方法使信号与干扰加上噪声之比达到了最大。与累积量基方法相比,这个方法的最突出优点有:方法简单,运算量少以及二阶相关矩阵易鲁棒地估计,因此更适合于在移动通信中应用。