一种基于四元十字麦克风阵的声源定位算法

实现了一种基于四元十字麦克风阵列的声源定位系统。选取四元十字阵作为麦克风阵列的阵型,推导了基于四元十字麦克风阵列的声源定位算法的公式。针对传统互相关时延估计算法在低信噪比、混响大的环境下鲁棒性较差的问题,系统采用广义互相关算法来进行定位的时延估计,并使用Cortex-A8嵌入式平台实现了鲁棒的声源定位系统。     

基于麦克风阵列的声源定位系统设计与应用

概括声源定位实验原理并设计麦克风阵列进行声源的捕捉,通过多次实验验证,与仿真软件的数据拟合,使得声源定位算法得到进一步提升。系统配备专门的上位机控制软件,当上位机得到各组麦克风阵列收集到的数据后,经Matlab程序估计时延及解析声源位置,通过软件的声源波形图和声源雷达图使得声源的定位更加直观。     

实时声源定位算法研究与实现

声源定位技术是语音增强、语音识别技术的前提和基础。基于麦克风阵列的声源定位技术已经成为一大研究热点,其广阔的应用前景得到了广泛的关注。本文提出基于变步长标准最小均方差（variable step size least mean square,VLMS）的声源定位算法。该算法利用VLMS算法自适应估计声源到麦克风的脉冲响应系数,进而估计出各麦克风之间时延,并利用几何方法定位声源在3D空间的位置。此外,本文设计了基于Cortex-A8嵌入式平台的声源定位系统,并进行了相应的硬件选型与调试及算法移植工作。实时实验显示,本系统的方案合理有效,能够较好的实现声源定位。     

基于传声器阵列的声源定位

采用基于时延估计(Time Delay Estimation,TDE)的声源定位技术,估计出目标声源到达不同麦克风阵元间的时间差,并结合平面五元十字阵的几何定位模型算法确定了声源位置。针对目前基于传声器阵列声源定位系统定位精度有待进一步提高的问题,提出一种新的定位算法,利用组合传声器阵列阵元相互之间几何位置关系,即通过多个传声器,多方向估计,多个阵列之间综合考虑,确定具有较高精度的声源位置。     

基于双麦克风的2维平面定位算法

基于麦克风阵列的声源定位技术受到了越来越多的关注。在视频会议、助听器、免提电话系统中,声源定位被用于检测说话人的位置信息来自动调节摄像头,或者形成波束。在各种声源定位方法中,基于到达时间差(time delay of arrival,TDOA)估计的双步定位算法是普遍采用的一种行之有效的方法。Birchfield从能量的角度出发提出了一种基于双耳电平差(interaural level difference,ILD)的双步定位算法,它通过检测多个麦克风对所接收到的信号能量比来确定声源的位置。然而,所有的这些方法如果要确定出声源在二维平面内的位置坐标,都至少需要三个麦克风。针对这一问题,本文提出了一种基于双麦克风的二维平面定位算法,类似于人的双耳定位原理,我们通过同时估计声源到达两个麦克风的能量比和时延信息,来达到定位的目的,而进一步推导出的闭合解可以用于实时地跟踪运动声源。最后的仿真结果证明了这一算法在一般的混响条件下都可以获得好的结果,然而它减小了阵列的尺寸,这对于体积受限的通信设备来说具有极大的吸引力。     

基于改进二次相关算法的声源定位仿真研究

基于广义相关时延算法的被动声源定位技术,其定位精度随着声源信号信噪比的降低而大幅下降,且易受噪声和混响等因素影响。针对此问题,该文提出了一种改进的二次相关时延算法,该算法引入了最小均方差（LMS）自适应滤波器作为前端处理以获得高质量的信号,并将信噪比作为控制参数对二次相关函数进行高次方运算,使二次相关函数峰值锐化,以提高当前广义二次相关算法的精度和鲁棒性。通过MATLAB进行不同信噪比环境下实验仿真,结果表明,该改进算法在低信噪比环境下能获得更精确的时延估计,进而有效提高了声源定位精度。     

一种基于麦克风阵列的声源定位算法研究

麦克风阵列声源定位广泛应用于视音频会议系统及枪声定位系统等领域。提出了一种基于最小熵值（ME）的麦克风阵列声源定位新方法,其特点在于利用最小熵值方法对麦克风阵列进行时延估计,并与离散网格方法相结合,对声源进行空间搜索。实验结果表明,在同等混响或噪声条件下,该方法定位优于广义互相关-相位变换方法（GCC-PHAT）。     

基于频率信噪比加权的麦克风阵列声源定位算法

为了提高噪声和混响环境下麦克风阵列的声源定位算法性能,提出了一种基于频率信噪比加权的可控响应功率定位算法。该算法首先根据每帧阵列信号的频域协方差矩阵估计每个频率的信噪比;然后通过激活函数将频率信噪比映射为加权值,并修正传统的相位变换可控响应功率计算公式;最后利用修正公式计算每个候选位置的可控响应功率值,通过搜索可控响应功率的最大值实现声源定位。该算法根据实时估计的频率信噪比自适应地调整各频率分量对可控响应功率的贡献。仿真结果表明,与传统的相位变换可控响应功率算法、维纳预滤波波束形成算法相比,在噪声和混响的复杂声学环境下,本文算法的定位正确率更高,均方根误差更小,对噪声的鲁棒性更强。      

基于Kinect的声源定位时延获取及算法性能研究

声源定位已广泛地应用于视频会议和语音控制系统中,针对传统的声源定位系统中采集电路采集到的声音信号易受噪音和混响的影响,提出采用运动传感输入设备Kinect中集成的小型线性麦克风阵列采集音频信号,通过Kinect采集四通道音频信号,并应用互相关(CC)、相位变换(PHAT)、最大似然估计(ML)和平均平方差函数(ASDF)等四种时延算法对采集的音频信号进行处理分析,从而获取时延实现声源的定位。另外,从信号-噪音比(SNR)、峰值锐化两个方面分别利用MATLAB仿真和实测实验比较了四种算法的性能。实验结果表明,利用Kinect线性麦克风阵列采集定位能更有效地去除噪声,提高信噪比,并且四种算法中相位变换法拥有尖锐的峰值和较低的信噪比门限,从而能够适用于声源定位中获取精确时延。     

复杂环境下基于时延估计的声源定位技术研究简

为了改善在复杂环境下声源定位算法的性能,提出了一种新的时延估计（TDE）方法,即基于传递函数比的统计模型方法（ATFR-SM）。该方法采用统计模型去除噪声对传递函数（ATF）的影响,在计算传递函数时对功率谱密度（PSD）进行平滑和“白化”,以去除混响对传递函数的影响。同时,算法中引入话音激活检测（VAD）去除对求取传递函数无用的噪声段,以提高时延估计的准确性。此外,将所提时延估计方法与线性定位法相结合,构成一套完整的声源定位方法。实验结果表明,在复杂环境下,时延估计方法具有更低的异常点百分比（PAP）和均方根误差（RMSE）,且明显优于传统的参考算法,同时声源定位方法具有更高的定位精度。     

Robust sound localization in 0.18 /spl mu/m CMOS

This paper presents a hardware implementation of a sound localization algorithm that localizes a single sound source by using the information gathered by two separated microphones. This is achieved through estimating the time delay of arrival (TDOA) of sound at the two microphones. We have used a TDOA algorithm known as the "phase transform" to minimize the effects of reverberations and noise from the environment. Simplifications to the chosen TDOA algorithm were made in order to replace complex operations, such as the cosine function, with less expensive ones, such as iterative additions. The custom digital signal processor implementing this algorithm was designed in a 0.18-/spl mu/m CMOS process and tested successfully. The test chip is capable of localizing the direction of a sound source within 2.2/spl deg/ of accuracy, utilizing approximately 30 mW of power and 6.25 mm/sup 2/ of silicon area.     

基于循环二次相关的时延估计

针对无源定位中时延估计的问题,在研究循环二次相关时延估计算法的基础上,结合希尔伯特差值时延估计算法,提出了一种新的时延估计方法。该方法运用希尔伯特差值法对循环二次相关峰值进行锐化处理,提高了时延估计精度,能在低信噪比条件下取得更好的时延估计性能。仿真验证了算法的有效性。     

TDOA中几种时延估计算法的比较

在现有的传声器阵列声源定位方法中,基于声达时间差(TDOA)估计定位法计算量较小,定位精度较高,同时也易于实现实时系统,是目前声源定位法中常用的方法。采用该方法最重要的就是进行时间延迟估计(TDE),其精确性直接影响到定位的准确与否。概括了基于传声器阵列的声达时间差(TDOA)估计定位法中几种时间延迟估计的算法,给出了部分算法的仿真结果,分析了每种算法中存在的优缺点并同时指出了需进一步研究的问题。     

基于DSP二维声源定向系统设计

利用ADSP BF533 DSP处理器设计了一种二维声源定向系统。系统基于声波到达时间差技术,采用相位匹配算法,对两个传声器采集的声音信号进行分析。通过算法仿真验证了算法的可行性和准确性,并将算法在DSP上实现。     

基于马尔科夫键蒙特卡洛抽样的最大似然时差-频差联合估计算法

该文针对无源定位中参考信号真实值未知的时差-频差联合估计问题,构建了一种新的时差-频差最大似然估计模型,并采用马尔科夫链蒙特卡洛(MCMC)方法求解似然函数的全局极大值,得到时差-频差联合估计。算法通过生成时差-频差样本,并统计样本均值得到估计值,克服了传统互模糊函数(CAF)算法只能得到时域和频域采样间隔整数倍估计值的问题,且不存在期望最大化(EM)等迭代算法的初值依赖和收敛问题。推导了时差-频差联合估计的克拉美罗界,并通过仿真实验表明,算法在不同信噪比条件下的估计精度优于CAF算法和EM算法,且计算复杂度较低。     

一种基于改进AMDF的时延估计方法

两路传感器接收信号的时延估计是时间到达差(TDOA)定位中需要解决的首要问题。多途效应是影响时延估计性能的重要因素。提出一种基于改进平均幅度差函数（AMDF）的时延估计方法,通过对信号进行多帧延拓与叠加,提高对多途效应的抑制能力。同时,结合广义互相关方法降低算法的噪声敏感性。仿真结果表明,该算法时延估计结果与传统方法相比具有更小的误差和更高的稳定性。     

Adaptive estimation of latency changes in evoked potentials

Changes in latency of evoked potentials (EP) may indicate clinically and diagnostically important changes in the status of the nervous system. A low signal-to-noise ratio of the EP signal makes it difficult to estimate small, transient, time-varying changes in latency, or delays. Here, the authors present an adaptive algorithm that estimates small delay (latency change) values even when EP signal amplitudes are time-varying. When the delay is time invariant, the adaptive algorithm produces an unbiased estimate with delay estimation error less than half of the sampling interval. A lower estimation error variance is obtained when, in a pair of signals, the adaptive algorithm delays the signal with the higher SNR. The adaptive delay estimation algorithm was tested on intra-operative recordings of somatosensory EP, and analysis of those recordings reveals that the anesthetic etomidate produces a step change in the amplitude and latency of the EP signals     

An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions

This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations.      

传感器位置误差情况下基于多维标度分析的时差定位算法

传统时差定位方法一般是在假设传感器位置信息准确已知的前提下进行的.然而在实际情形中,传感器位置信息往往含有随机误差,这些误差会严重影响对目标的定位精度.针对这一问题,提出了一种传感器位置误差情况下的多维标度时差定位算法.首先利用传感器位置和时差构造对称标量积矩阵,然后利用子空间理论建立关于目标位置的伪线性方程,最后通过设计加权矩阵来减少传感器位置误差对目标定位精度的影响.采用一阶小噪声扰动理论求出了目标位置估计的偏差及协方差矩阵,并通过仿真实验验证了该算法的有效性.     

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.