Acoustic analysis by spherical microphone array processing of room impulse responses

Spherical microphone arrays have been recently used for room acoustics analysis, to detect the direction-of-arrival of early room reflections, and compute directional room impulse responses and other spatial room acoustics parameters. Previous works presented methods for room acoustics analysis using spherical arrays that are based on beamforming, e.g., delay-and-sum, regular beamforming, and Dolph-Chebyshev beamforming. Although beamforming methods provide useful directional selectivity, optimal array processing methods can provide enhanced performance. However, these algorithms require an array cross-spectrum matrix with a full rank, while array data based on room impulse responses may not satisfy this condition due to the single frame data. This paper presents a smoothing technique for the cross-spectrum matrix in the frequency domain, designed for spherical microphone arrays, that can solve the problem of low rank when using room impulse response data, therefore facilitating the use of optimal array processing methods. Frequency smoothing is shown to be performed effectively using spherical arrays, due to the decoupling of frequency and angular components in the spherical harmonics domain. Experimental study with data measured in a real auditorium illustrates the performance of optimal array processing methods such as MUSIC and MVDR compared to beamforming.     

协方差矩阵重构的稳健自适应波束形成算法

针对协方差矩阵含有期望信号成分以及波束指向角失配时,传统自适应波束形成器性能严重下降的问题,提出了协方差矩阵重构的稳健自适应波束形成算法。该算法将全空域划分成若干互不重叠的区域,分别对应干扰区域与信号区域,先利用Capon波束形成器对干扰区域积分,由此构造出干扰协方差矩阵。然后,利用标准Capon波束形成器的波束域MUSIC谱估计法对信号区域积分,重构出信号协方差矩阵,以其主特征向量作为期望信号导引向量估计。由于算法重构了干扰加噪声协方差矩阵并对导引向量进行了修正,保证了自适应波束形成器的性能。理论分析和仿真实验结果表明,算法在训练数据含有期望信号成分和波束指向角度失配情况下具有良好的性能。      

快速收敛最小方差无畸变响应算法研究及应用

常规最小方差无畸变响应(MVDR)自适应波束形成是一种高分辨窄带波束形成器,它是利用实际声场的窄带互谱密度矩阵(CSDM)估计出自适应波束形成权向量。在实际应用中,MVDR算法需要较长的观测时间估计协方差矩阵,不利于对高速运动目标进行定位;对于宽带目标信号,MVDR算法需要对每一个CSDM进行求逆运算,计算量较大;在相干源条件下,目标信号之间会发生"对消"现象,MVDR算法性能急剧恶化。本文提出了基于子带子阵处理的快速收敛MVDR自适应波束形成方法。首先将全频带划分成一组子带,将接收线阵划分成一组子阵,然后对每一子带计算降维的驾驶协方差矩阵(STCM),从而得到快速收敛MVDR自适应波束形成的权值和空间谱估计结果。同时采用双向空间平滑方法对相干源进行MVDR空间谱估计。仿真和海试数据处理结果表明该算法在保证高分辨力的同时,具有瞬时收敛的性能,双向空间平滑技术具有良好的解相干性能。      

基于矢量水听器阵列空域预滤波处理的多重信号分类算法研究

矢量水听器能同时拾取声压和振速信息,在相同的信噪比、阵元数及阵列孔径下,矢量阵定向性能优于声压阵列。目前,以多重信号分类算法(Multiple signal classification,MUSIC)为代表的高分辨定向算法已经广泛应用于矢量水听器阵列中。但是随着信噪比降低、信号源方位间隔减小,传统MUSIC算法定向精度及分辨概率显著下降。本文采用最小二乘法设计适用于矢量水听器水平阵列的矩阵空域滤波器,用于阵列数据的空间滤波预处理,可以对阻带扇面噪声进行有效抑制。由滤波后的数据协方差矩阵可以得到新的噪声子空间,在传统MUSIC算法基础上修正通带扇面内阵列流型的畸变后即可得到滤波后MUSIC算法的方位谱。仿真结果表明,当信噪比较低时,改进算法有效提高了通带扇面内目标方位分辨性能。最后本文对四基元矢量水平阵列海试数据进行了处理,改进算法对窄带信号定向较常规算法-3 dB束宽减小了13°,旁瓣级降低约8 dB。对有一定带宽的行船辐射噪声定向处理得到了更加精确的航迹图,海试数据处理结果证明了该算法的可行性和有效性。     

交叉验证多路径匹配追踪水声矢量阵稀疏方位估计

水下运动目标的高分辨DOA估计和目标的左右舷分辨问题一直是水声阵列信号处理中的一个核心问题。矢量阵相比于声压阵具有天然的左右舷分辨能力和更高的处理增益,近年来得到了广泛关注。Capon等一些传统高分辨处理方法存在不能解相干源、需要多快拍处理以及对阵列流行误差敏感等多种问题。针对水声阵列信号处理领域面临的以上问题,利用声呐工作场景中空间目标的稀疏性,本文提出了一种基于交叉验证技术的多路径匹配追踪（Multiplepath Matching Pursuit with Cross Validation,CV-MMP）声矢量阵稀疏DOA估计算法。该算法采用交叉验证技术可以在未知场景中目标个数的条件下实现稀疏DOA的估计,相比于常规的声矢量阵Capon算法而言,可以在小快拍数甚至单快拍数条件下实现多目标的稀疏DOA估计以及高分辨能力。仿真和海试试验数据处理验证了提出的算法的有效性。      

Experimental subwavelength localization of scatterers by decomposition of the time reversal operator interpreted as a covariance matrix

The D.O.R.T. method (French acronym for Decomposition of the Time Reversal Operator) is an active remote sensing technique using arrays of antennas for the detection and localization of scatterers [Prada et at., J. Acoust. Soc. Am. 99, 2067-2076 (1996)]. The analogy between the time reversal operator and the covariance matrix used for classical sources separation in passive remote sensing [Bienvenu et al., IEEE Trans. ASSP 31, 1235-1247 (1983)] is established. Then, an experiment of subwavelength detection and localization of point-like scatterers with a linear array of transducers is presented. Using classical estimators in reception like Maximum-Likelihood and Multiple Signal Characterization (MUSIC), two point-like scatterers separated by lambda/3 and placed at 100lambda from the array of transducers are resolved. In these experiments, the role of multiple scattering and the existence of additional eigenvectors associated with dipolar and monopolar radiation of each scatterer is discussed.     

Implementation issues of the nearfield equivalent source imaging microphone array

This paper revisits a nearfield microphone array technique termed nearfield equivalent source imaging (NESI) proposed previously. In particular, various issues concerning the implementation of the NESI algorithm are examined. The NESI can be implemented in both the time domain and the frequency domain. Acoustical variables including sound pressure, particle velocity, active intensity and sound power are calculated by using multichannel inverse filters. Issues concerning sensor deployment are also investigated for the nearfield array. The uniform array outperformed a random array previously optimized for far-field imaging, which contradicts the conventional wisdom in far-field arrays. For applications in which only a patch array with scarce sensors is available, a virtual microphone approach is employed to ameliorate edge effects using extrapolation and to improve imaging resolution using interpolation. To enhance the processing efficiency of the time-domain NESI, an eigensystem realization algorithm (ERA) is developed. Several filtering methods are compared in terms of computational complexity. Significant saving on computations can be achieved using ERA and the frequency-domain NESI, as compared to the traditional method. The NESI technique was also experimentally validated using practical sources including a 125 cc scooter and a wooden box model with a loudspeaker fitted inside. The NESI technique proved effective in identifying broadband and non-stationary sources produced by the sources.     

Kalman filter-based microphone array signal processing using the equivalent source model

This paper demonstrates that microphone array signal processing can be implemented by using adaptive model-based filtering approaches. Nearfield and farfield sound propagation models are formulated into state-space forms in light of the Equivalent Source Method (ESM). In the model, the unknown source amplitudes of the virtual sources are adaptively estimated by using Kalman filters (KFs). The nearfield array aimed at noise source identification is based on a Multiple-Input–Multiple-Output (MIMO) state-space model with minimal realization, whereas the farfield array technique aimed at speech quality enhancement is based on a Single-Input–Multiple-Output (SIMO) state-space model. Performance of the nearfield array is evaluated in terms of relative error of the velocity reconstructed on the actual source surface. Numerical simulations for the nearfield array were conducted with a baffled planar piston source. From the error metric, the proposed KF algorithm proved effective in identifying noise sources. Objective simulations and subjective experiments are undertaken to validate the proposed farfield arrays in comparison with two conventional methods. The results of objective tests indicated that the farfield arrays significantly enhanced the speech quality and word recognition rate. The results of subjective tests post-processed with the analysis of variance (ANOVA) and a post-hoc Fisher's least significant difference (LSD) test have shown great promise in the KF-based microphone array signal processing techniques.     

近似窄带假设下的最小方差无失真响应波束形成

最小方差无失真响应波束形成算法在应用于语音等宽带信号时,依赖窄带假设可以在频域各个子带分别进行滤波。窄带假设下语音信号协方差矩阵是秩-1矩阵,而实际中窄带信号模型只是实际信号模型的一种近似,同时由于存在统计量估计误差,估计的语音信号协方差矩阵的秩一般大于1。提出利用语音协方差矩阵和噪声协方差矩阵的广义主特征向量来估计相对传递函数,用于重构语音信号协方差矩阵为秩-1矩阵。在REVERB数据集以及CHiME-4数据集上进行实验验证,最小方差无失真响应波束形成算法经过语音协方差矩阵低秩近似后,对估计误差的鲁棒性提高,输出信噪比分别提升平均0.8 dB和1.4 dB,同时提升了语音识别准确率。      

Localization of multiple acoustic sources with small arrays using a coherence test

Direction finding of more sources than sensors is appealing in situations with small sensor arrays. Potential applications include surveillance, teleconferencing, and auditory scene analysis for hearing aids. A new technique for time-frequency-sparse sources, such as speech and vehicle sounds, uses a coherence test to identify low-rank time-frequency bins. These low-rank bins are processed in one of two ways: (1) narrowband spatial spectrum estimation at each bin followed by summation of directional spectra across time and frequency or (2) clustering low-rank covariance matrices, averaging covariance matrices within clusters, and narrowband spatial spectrum estimation of each cluster. Experimental results with omnidirectional microphones and colocated directional microphones demonstrate the algorithm's ability to localize 3-5 simultaneous speech sources over 4 s with 2-3 microphones to less than 1 degree of error, and the ability to localize simultaneously two moving military vehicles and small arms gunfire.     

小快拍高分辨目标方位估计算法 GMUSIC的性能分析*

针对水下运动阵列在运动过程中进行方位估计时存在快拍不足的问题,研究了基于随机矩阵理论的MUSIC改进算法GMUSIC,该方法通过Stieltjes变换建立起统计协方差矩阵真实特征值、特征向量与样本协方差矩阵之间在逼近域中的关联,以修正样本协方差特征分解的结果,进而实现小快拍方位估计。仿真与试验表明:GMUSIC算法可以更好地分辨相邻目标,且需要的快拍数较MUSIC算法要少;在低信噪比情况下,GMUSIC算法方位估计均方根误差远小于MUSIC算法,估计成功概率远大于MUSIC算法。因此,GMUSIC算法适用于解决水声目标的小快拍方位估计问题。     

Waveguide invariant focusing for broadband beamforming in an oceanic waveguide

The performance of broadband sonar array processing can degrade significantly in shallow-water environments when interference becomes angularly spread due to multipath propagation. Particularly for towed line arrays near endfire, elevation angle spreading of multipath interference often results in masking of weaker sources of interest. While adaptive beamforming in a series of narrow frequency bands can suppress coherent multipath interference, this approach requires long observation times to estimate the required narrowband covariance matrices. To form wideband covariance matrices which can be estimated with less observation time, plane-wave focusing methods have been used to avoid interference covariance matrix rank inflation. This paper extends wideband focusing to the case of coherent multipath interference. The approach presented here, called waveguide invariant focusing (WIF), exploits a robust relationship for the frequency dependence of horizontal wave number differences. Unlike matched-field methods, WIF does not model multipath wave fronts but rather makes the interference appear to occupy the same rank-one subspace across frequency. This permits formation of wideband covariance matrices without interference rank inflation. Simulation experiments in a realistic ocean environment indicate that adaptive beamforming using WIF covariance matrices can provide a significant array gain improvement over conventional adaptive methods with limited observation time.     

基于多个小孔径基阵的水下目标宽带波达方位估计方法

本文将多小孔径基阵应用于声纳浮标阵对可疑海区进行快速搜索的研究中,提出了一种基于多个小孔径子阵的宽带目标方位估计方法.该方法采用一种新颖的降秩估计器的信号模型,对多个小孔径子阵的输出信息进行综合处理,然后使用近似意义上的降秩估计器对目标源进行方位估计.所建立的阵列模型中不需要包含有关小孔径子阵之间的任何位置信息,因此,有效地减小了因阵形估计带来的烦杂工作和估计误差对方位估计性能的影响.仿真结果表明,此方法提高了定向精度,给出了稳定的目标方位估计.     

球形传声器阵列下基于主导声源检测MUSIC群时延算法的多声源定位*

针对混响环境中,多径效应、散射、衍射等原因导致声源定位失败或分辨能力不足的现象,提出一种基于主导声源检测MUSIC群时延的邻近多声源定位方法。该方法采用球形传声器阵列,相比平面阵列可以捕获3D声场信息,利用球谐域下信号的频率分量与角度分量解耦的优势,从而可直接利用频率平滑技术处理宽带语声信号而不需要构造聚焦矩阵,并在球谐域下通过设置阈值对一组时频段进行主导声源检测,从而选择出包含直达声的一组时频块来构造MUSIC群时延空间谱。上述举措在提升波达方向估计在高混响环境下定位鲁棒性的同时,也提高了多个邻近声源的分辨能力。仿真实验结果表明,所提出的主导声源检测MUSIC群时延算法,在高混响和低信噪比条件下,仍具有更好的定位精度与更优的邻近多声源分辨效果。     

Adaptive Signal Processing in Antenna Arrays with Allowance for the Rank of the Impulse-Response Matrix of a Multipath Channel

We consider adaptive spatial signal processing in antenna arrays under conditions of multipath propagation. It is shown that estimation of the elements of the matrix of multichannel impulse response (MCIR) in the receiving channels of antenna arrays is not sufficient for performing such a processing, and we should also determine the MCIR-matrix rank, which is equal to the minimum number of adaptive spatial channels for reception of a multipath signal. We propose a threshold method for estimating the rank of the MCIR matrix on the basis of the statistical properties of maximum-likelihood estimation of the elements of this matrix and a priori information on the properties of internal noise in the receiving channels of the system. The weight vectors of the beamformer processor ensuring adaptive spatial signal processing are estimated.     

MASS-RAB: Robust adaptive beamforming for general-rank signal models via matched spatial spectrum processing

The wavefront of acoustic signal suffers from fast fluctuation after a long distance propagation in a random and inhomogeneous ocean channel, which makes the rank of the covariance matrix for the desired signal (signal of interest) remarkably higher than one. Consequently, the assumption of rank-one point signal model for existing adaptive beamforming algorithms is no longer suitable. In this paper, a matched spatial spectrum processing based robust adaptive beamforming (MASS-RAB) algorithm is presented for general-rank signal models. First, the interference-plus-noise covariance matrix and the desired signal covariance matrix are reconstructed using the matched spatial spectrum processing method. Second, the weight vector is directly calculated using these reconstructed covariance matrices for the minimum variance distortionless response (MVDR) algorithm, which is developed for the general-rank signal models. Due to covariance matrix reconstruction, the MASS-RAB algorithm is more robust than those methods relying on the sample covariance matrix. The cases of the rank-one point signal model and the full-rank non-point signal model are considered by several numerical examples. Experimental results have demonstrated the superiority of the proposed MASS-RAB method.     

Estimation of the background noise level with a horizontal array against spatially uncorrelated and structural interferences

A method of estimating the isotropic sea noise level with a horizontal array in the presence of uncorrelated interference and interference with a complex spatial structure is proposed and experimentally tested. The algorithm is based on the approximation of the Capon spatial spectrum of the received signal using a model Capon spectrum for the sum of isotropic noise and uncorrelated interference. A numerical simulation is carried out to study the dependence of the accuracy of the proposed method on the interference intensity, the distance from the array to the sources of structural interference, and the number of structural interference sources. It is shown that the use of the Capon spectrum provides a strong suppression of an intense structural interference source positioned near the array. The efficiency of the proposed method is confirmed experimentally.     

Efficient tensor decomposition method for noncircular source in colocated coprime MIMO radar

An effective method via tensor decomposition is proposed to deal with the joint direction-of-departure(DOD) and direction-of-arrival(DOA) estimation of noncircular sources in colocated coprime MIMO radar. By decomposing the transmitter and receiver into two sparse subarrays, noncircular property of source can be used to construct new extended received signal model for two sparse subarrays. The new received model can double the virtual array aperture due to the elliptic covariance of imping sources is nonzero. To further exploit the multidimensional structure of the noncircular received model, we stack the subarray output and its conjugation according to mode-1 unfolding and mode-2 unfolding of a third-order tensor, respectively. Thus, the corresponding extended tensor model consisted of noncircular information for DOA and DOD can be obtained. Then, the higher-order singular value decomposition technique is utilized to estimate the accurate signal subspace and angular parameter can be automatically paired via the rotational invariance relationship.Specifically, the ambiguous angle can be eliminated and the true targets can be achieved with the aid of the coprime property.Furthermore, a closed-form expression for the deterministic CRB under the NC sources scenario is also derived. Simulation results verify the superiority of the proposed estimator.     

Robust adaptive acoustic vector sensor beamforming using automated diagonal loading

In this paper, a novel robust adaptive acoustic vector sensor beamformer based on shrinkage is derived. Unlike many existing methods, the proposed method is completely automatic (or so-called user parameter-free), which means, it do not need the choice of user parameters. The proposed diagonal loading algorithms use shrinkage-based covariance matrix estimates, instead of the conventional sample covariance matrix, in the standard Capon acoustic vector sensor beamforming formulation. The numerical results show that our method is robust against errors on the steering vector and small sample sizes, and meanwhile gives high output signal to interference plus noise ratio (SINR).     

用于裂纹方向识别的超声散射系数分析方法研究

本文主要利用超声相控阵技术进行了裂纹方向识别研究。首先,对线性超声相控阵探头采集的全矩阵数据进行了全聚焦成像,确定缺陷的位置。然后将线性阵列划分为若干子阵列,研究了缺陷位置处子阵列的散射系数分布,从中提取出缺陷的方向信息。在此基础上,研究了子阵列参数选择(子阵列包含晶片数及相邻子阵列间隔晶片数)及探头位置对裂纹方向识别的影响。通过对散射系数分布图中提取的3个特征指标,角度测量误差、角度分辨率及相对脊带宽度,进行主成分分析,评价了子阵列参数设置(如子阵列包含晶片数及相邻子阵列间隔晶片数)对裂纹方向识别的影响,优化出最佳的检测位置及子阵列参数设置。仿真和检测实验结果表明,当相邻子阵列间隔晶片数为1,包含晶片数为11个时,可以利用超声散射系数分布进行裂纹方向的准确测量,测量误差小于2%。