未知噪声背景下多声源方位角的估计方法

以平面波、窄带声源的阵列测向模型为基础,利用阵列信号处理的理论和方法,在假设理想的白噪声背景下,对均匀线阵接收的信号数据,采用多信号分类法（ＭＵＳＩＣ法）,利用其正交性的谱估计来判断声源的方位角。在此基础上,对不少实际环境中的噪声模型是非白和不确定的情况下,提出一种辅助阵元的方法,以确定在未知噪声背景下利用ＭＵＳＩＣ法确定多声源的方位角。声学实验证实,在实际环境中,该方法能很好地分辨多声源的方位角,且标准差很小。通过与假想的白噪声环境进行对比,本文方法能有效地消除实际环境带来的测向误差,大大提高测向分辨率,由此证实了该方法的有效性。     

正四面体麦克风阵列声源定位模型研究

研究声源定位优化建模问题,针对声源位于远场环境下无法获取精确的方位角和俯仰角,由于采用声达时间差(TDOA)和空间几何算法的正四面体麦克风阵列声源定位方法只适应于近场声源定位,为了提高定位准确性,提出了应用径向基(RBF)神经网络建立声源定位模型的算法,声源定位模型在声源位于近场或者远场的情况下,均可求解出精确的方位角和俯仰角。在MATLAB上进行仿真,结果表明,定位声源的方位角误差小于3°,俯仰角误差小于4°,满足实际定位精度的要求。结果表明为声源准确定位提供了科学依据。     

基于BP神经网络的正四面体阵列声源定向研究

针对利用正四面体麦克风阵列获取的时延值实现目标声源跟踪这个问题,提出了一种基于BP神经网络的声源定向方法。设计了一个含有双隐层的BP神经网络,使用Matlab神经网络工具箱进行仿真实验,证明可以实现远场和近场的声源定向,进而进行声源跟踪,有较高的实用性。     

基于麦克风阵列的声源定位算法研究

在基于时延估计的声源定位系统中,由于定位算法分为两个阶段:时延估计和定位,时延估计阶段的误差会在定位阶段被放大,导致声源定位的成功率和精度较低。从原始信号去噪,时延值插值和定位算法三方面入手,提高声源定位的精度。结合自行设计的四元十字麦克风阵列,给出一种新的时延值筛选算法,实现了一个室内声源定位系统。实验结果表明,在二维定位场景中,该系统对声源方位角的估计成功率超过70%,平均误差小于5°;该系统对声源距离估计的成功率和精度与声源方位角有关,当声源方位与X、Y轴的夹角不超过15°且声源距离不超过2.5 m时,声源距离估计的成功率能达到50%以上。     

带支撑物圆阵阵列信号处理算法及仿真分析

传统阵列信号处理算法一般都不考虑障碍物对阵列响应的影响,实际中阵列支撑物会对阵列接收信号会产生影响。以环绕在刚性圆柱体上的均匀圆阵为阵列模型,研究了特征波束空间波束形成和高分辨方位角估计算法性能,分析了各种因素对算法性能的影响。研究结果说明,特征波束域高分辨方位角估计算法能有效地估计出多个声源的方位角,阵列支撑物的存在提高了特征波束形成和高分辨方位角估计算法性能,为方位角估计提供了一种有效的方法。     

近场相干声源三维定位MUSIC算法

近场环境下传统多重信号分类(Multiple Signal Classification,MUSIC)算法无法对相干声源进行三维位置估计,针对该问题论文提出了一种近场相干声源三维定位算法.首先建立了近场球面波信号接收模型,其次结合空间平滑算法和修正MUSIC算法将矩形阵列分割成数个子阵列,对其协方差矩阵和求复共轭并左右同乘单位矩阵,以此来解决相干信号协方差矩阵秩亏损的问题,最后使用三维MUSIC算法完成近场声源三维位置估计.仿真表明:论文算法可有效地对相干信号进行解相干处理并准确地完成近场相干声源的三维位置估计.     

基于差分麦克风阵列和语音稀疏性的多源方位估计方法

差分麦克风阵列为实现小尺寸阵列条件下的声源定位提供了一条重要技术途径。语音信号具有稀疏性,利用该特性可实现基于差分麦克风阵列的多声源方位估计,其中的典型方法为直方图法。针对差分麦克风阵列,本文提出了一种基于时频掩蔽和模糊聚类分析的短时平均复声强多声源方位估计方法。分析了不同阵列尺寸条件下时频掩蔽频带范围的选择问题。该方法具有闭式解,在强混响噪声环境下的性能优于直方图法,并且受阵列尺寸变化的影响较小。为了改善直方图法的性能, 基于时频掩蔽的思想,文中还给出了一种修正的直方图方法。混响噪声环境下的仿真实验结果验证了本文所提方法的有效性。     

基于七元传声器阵列的声源定位算法及性能分析

为了准确定位声源所在空间位置,提高声源定位性能,在分析方位估计算法的基础上,建立七元传声器阵列模型,提出一种声源定位算法。根据阵元间的矢量关系,推导出声源方位计算公式,实现声源定位。利用阵列参数,水平偏角、仰角和声源到阵元中心距离,与定位性能关系,对测距测向精度进行分析。结果表明,该算法声源坐标误差为1.0%,方位角误差为0.5%,具有较好的定位效果。     

智能视频监控系统的声源定位设计与实现

在视频监控系统优化设计的研究中,当前的智能视频监控采用图像处理的跟踪方法.由于摄像头的视角有限,系统存在目标不在视场范围内的监控盲区.为避免上述缺陷,通过声学相控阵给摄像头加上了听觉功能,使监控系统能够自动跟踪声源方位.系统采用麦克风线性阵列接收音频信号,通过端点检测实现有用音频信号的实时检测,通过频域波束形成实现对宽带音频信号的空间定位,最后采用能量值的谱搜索算法定位出声源的方位.前期的仿真和后期基于DSP的嵌入式系统平台实验均验证了改进方法的可行性和工程应用价值.     

基于粒子滤波的声源方位跟踪算法

针对混响环境中说话人方位跟踪问题,提出一种基于粒子滤波的声源方位跟踪算法。该算法根据说话人的运动特点,在Langevin方程的基础上构建声源方位的动态模型,采用相位变换加权的可控响应功率作为定位函数,运用粒子滤波对声源方位进行跟踪。使用典型会议室环境下小型麦克风阵列接收的真实数据来做实验。结果表明,该算法能有效地实现随机走动说话人的方位跟踪,并且在水平角和仰角方向的均方根误差均小于5°。     

一种鲁棒的双耳声源方位角定位方法

在噪声和混响的声学环境中,基于双耳时间差的声源方位角定位性能会严重降低。针对这个问题,提出了一种基于子带选择和DBSCAN的双耳声源定位算法,首先,采用 Gammatone 滤波器将双耳声源信号分解为若干个子带信号;其次,根据子带能量大小进行子带通道数压缩;然后,根据子带信噪比大小获取最优子带,降低无关子带干扰;接着将子带信号进行分帧,根据互相关算法获取峰值处的数据点;最后,引入DBSCAN算法消除噪声点的影响,获取最优数据点,从而根据ITD定位模型判断目标声源方位角,实验结果表明,该算法在复杂的声学环境中,相较于传统的互相关算法,可显著提高双耳声源方位角定位性能。     

Two-Dimensional DOA Estimation of Sound Sources Based on Weighted Wiener Gain Exploiting Two-Directional Microphones

We propose a new method for estimating directions of arrival (DOAs) of sound sources, both in azimuthal and elevation angle, using two directional microphones. This method adopts weighted Wiener gain (WWG) for DOA estimation. WWG is an estimate of the Wiener gain that we proposed for use in automatic gain control to enhance speech that is degraded by additive noise. Angular resolution of WWG arises from spectral subtraction (SS)-based noise reduction involved in the WWG calculation, which enhances the signal from the look direction while suppressing signals from other directions. Because WWG involves two-channel SS, which can deal with instantaneous noise, noise sources need not to be stationary, as they must be with ordinary single-channel SS. We further propose the exploitation of a pair of directional microphones whose front directions are arranged in rotational symmetry. The time difference and amplitude difference between the two-channel signal provided by the microphones are utilized to yield a two-dimensional resolution of DOA. We evaluated the proposed method through computer simulations and compared it to three DOA estimation methods that are based on a cross-correlation function and two popular high-resolution methods of multiple signal classification and minimum variance method. Evaluation results of the source detection rate and estimation accuracy demonstrate the remarkable superiority of our method compared to the other methods in conditions where multiple speech sources exist     

声音与震动阵列的分析、实现与应用

In this paper, we consider the analysis, implementation, and application of wideband sources using both seismic and acoustic sensors. We use the approximate maximum likelihood (AML) algorithm to perform acoustic direction of arrival (DOA). For non-uniform noise spectra, whitening filtering was applied to the received acoustic signals before the AML operation. For short-range seismic DOA applications, one method was based on eigen-decomposition of the covariance matrix and a second method was based on surface wave analysis. Two well-known optimization schemes were used to estimate the source locations from the estimated DOAs at sensors of known locations. Experimental estimation of the DOAs and resulting localizations using the acoustic and seismic signals generated by striking a heavy metal plate by a hammer were reported.     

一种混合源环境下的波达方向估计方法

提出了一种基于均匀线阵的混合源波达方向DOA估计的新方法。该方法首先利用传统MUSIC方法估计出非相干信号源的DOA,然后对整个阵列数据协方差矩阵进行差分消除不相关源信号和噪声的影响,再对此差分矩阵进行特殊的空间平滑去相干,利用重建的数据协方差矩阵估计相干源的DOA。此方法的特点是分别估计不相关信号和相干信号的DOA,优点是在可估计出多于阵元数信号的前提下具有较高的DOA估计精度和稳健性。理论分析和仿真结果表明此方法的估计性能优于空间差分平滑算法。     

A distributed design for multiple moving source positioning

Acoustic source localization has many important applications particularly for military tracking foreign objects. Even though Wireless Sensor Networks (WSNs) have been developed, this localization problem remains a big challenge. A system for solving source localization must have the ability to deal with the problems of recorded convolved mixture signals while minimizing the high communication and computation cost. This paper introduces a distributed design for positioning multiple independent moving sources based on acoustic signals in which we focus on utilizing the relative information of magnitudes recorded at different sensors. The sensors perform preprocessing on the sensed data to capture the most important information before compressing and sending extracted data to the base. At the base, the data is uncompressed and the source locations are inferred via two clustering stages and an optimization method. Analysis and simulation results lead to the conclusion that our system provides good accuracy and needs neither much communication nor complex computation in a distributed manner. It works well when there exists high noise with Rayleigh multipath fading under Doppler effect and even when the number of independent sources is greater than the number of microphone sensors.     

基于双麦克风的室内语音分离与声源定位系统

为了探究利用两个麦克风进行多声源分离和二维平面定位的可能性,提出了一种基于双麦克风的室内语音分离与声源定位系统。该系统根据麦克风采集的信号,建立了双麦克风时延-衰减模型,然后利用DUET算法估计了模型的时延-衰减参数,并绘制了参数直方图。在语音分离阶段,建立了二进制时频掩膜（BTFM）,根据参数直方图,结合二值掩蔽的方法对混合语音进行了分离;在声源定位阶段,通过推导模型衰减参数与信号能量比之间的关系,得到了确定声源位置的数学方程组。利用Roomsimove工具箱模拟室内声学环境,通过Matlab仿真和几何坐标计算,在对多个声源目标分离的同时完成了二维平面中的定位。实验结果表明,该系统对多个声源信号的定位误差均在2%以下,有助于小型系统的研究和开发。     

Simultaneous Localization of a Mobile Robot and Multiple Sound Sources Using a Microphone Array

Sound source localization is an important function in robot audition. Most existing works perform sound source localization using static microphone arrays. This work proposes a framework that simultaneously localizes the mobile robot and multiple sound sources using a microphone array on the robot. First, an eigenstructure-based generalized cross-correlation method for estimating time delays between microphones under multi-source environments is described. Using the estimated time delays, a method to compute the farfield source directions as well as the speed of sound is proposed. In addition, the correctness of the sound speed estimate is utilized to eliminate spurious sources, which greatly enhances the robustness of sound source detection. The arrival angles of the detected sound sources are used as observations in a bearing-only simultaneous localization and mapping procedure. As the source signals are not persistent and there is no identification of the signal content, data association is unknown and it is solved using the FastSLAM algorithm. The experimental results demonstrate the effectiveness of the proposed method.     

A model of co-saliency based audio attention

Inspired by biological perceptual characteristics in human auditory systems and the mechanisms of saliency detection, we study the relevance constraint between time-frequency characteristics of sound signals and the multiple spectrogram and propose a co-saliency detection method for multiple sound signals in this paper. Then, according to the auditory characteristics of the human ear, the distinctive saliency features from the acoustic channel and the image channel are fused. Finally, an auditory saliency map is obtained to complete the detection of significant sounds. The saliency features of the acoustic channel include the features calculated in the in the temporal and spectral domains of signal, which the temporal saliency features could be represented by the local maximum points in the Power Spectral Density (PSD) curve, and the spectral features could be represented by local maximum points in Mel Frequency Cepstrum Coefficient (MFCC) curve of sound signal. The saliency features of acoustic channel and cross-scale fusion with the contrast cue of spectrogram, whose result is more in line with the human auditory attention mechanism. Finally, combined with the corresponding cue which could reflect the distribution between multiple spectrograms, it could reflect the characteristics of global repeatability, and reflect high frequency of occurrence. Experimentally, the auditory Co-Saliency map verifies the accuracy and robustness of proposed method in this paper. It shows that the proposed method is superior to other traditional detection methods for auditory saliency, and can implement intelligent automatic detection to sound signals.

     

Sound and Visual Tracking for Humanoid Robot

Mobile robots capable of auditory perception usually adopt the stop-perceive-act principle to avoid sounds made during moving due to motor noise. Although this principle reduces the complexity of the problems involved in auditory processing for mobile robots, it restricts their capabilities of auditory processing. In this paper, sound and visual tracking are investigated to compensate each other's drawbacks in tracking objects and to attain robust object tracking. Visual tracking may be difficult in case of occlusion, while sound tracking may be ambiguous in localization due to the nature of auditory processing. For this purpose, we present an active audition system for humanoid robot. The audition system of the highly intelligent humanoid requires localization of sound sources and identification of meanings of the sound in the auditory scene. The active audition reported in this paper focuses on improved sound source tracking by integrating audition, vision, and motor control. Given the multiple sound sources in the auditory scene, SIG the humanoid actively moves its head to improve localization by aligning microphones orthogonal to the sound source and by capturing the possible sound sources by vision. The system adaptively cancels motor noises using motor control signals. The experimental result demonstrates the effectiveness of sound and visual tracking.     

基于高阶零陷滤波的空间多目标跟踪算法

针对传统的移动多目标跟踪算法计算量大、实时性差的问题, 提出了一种新的基于阵列天线的空间多目标跟踪算法。算法利用阵元个数相对于信源数目的自由度, 设计一个高阶的零陷空域滤波器组, 对空间干扰源进行陷波, 并对空间波束成形后的信号进行自适应跟踪, 估计出多个移动目标的波达方向（direction of arrival, DOA）。仿真结果表明, 此算法精度较高, 计算复杂度较低, 为空间移动多目标的实时跟踪提供了一种新方法。