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1.
In this paper a novel method for tracking an active speaker in a noisy and reverberant environment by means of a spatially distributed microphone array is presented. Firstly, a sound source localization algorithm based on time delays of arrival (TDOA) in microphone pairs provides observed position estimates. Then these remarkably noisy estimates are filtered by a multiple model Kalman filter (MMKF) in order to obtain a smoothed trajectory of the speaker’s movement. Compared with the traditional Kalman filter (KF), simulated results prove the MMKF is more robust and effective in noisy environments.  相似文献   

2.
To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing(CS) theory.In this method two-step discrete cosine transform(DCT)-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning(DL) method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio(SNR).The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.  相似文献   

3.
In microphone arrays application, it is a difficult task to accurately and fast localize sound source in a noisy, reverberant environment. In order to solve this problem, many approaches have been presented. Among them, the steered response power-phase transform weighted (SRP–PHAT) source localization algorithm has been proved robust. However, SRP–PHAT requires high computation cost for searching a large location space. To overcome this shortcoming, an improved SRP–PHAT will be presented that reduces a two-dimension searching space into a couple of one-dimension ones by using an orthogonal linear array. In this method, the parameters of direction of arrival (DOA) are separated. The main computation can be carried out independently in two one-dimension spaces, thus the computational load will be greatly cut down. Simulations show that there is no loss in accuracy in the proposed method.  相似文献   

4.
一种利用分布式传声器阵列的声源三维定位方法   总被引:1,自引:0,他引:1       下载免费PDF全文
柯炜  张铭  张铁成 《声学学报》2017,42(3):361-369
为了提高噪声和混响条件下分布式传声器阵列进行声源定位的性能,提出一种利用空间稀疏性和压缩感知原理的声源三维定位方法。该方法首先通过两次离散余弦变换方式提取出声音信号特征,并用该特征来构建稀疏定位模型,以便能够综合利用语音信号的短时和长时特性,同时降低模型维数;然后利用在线字典学习技术动态调整字典,克服稀疏模型与实际信号之间的失配问题,增强稀疏定位模型的鲁棒性;进而提出一种改进的平滑l0范数稀疏重构算法来进行声源位置解算,以提高低信噪比条件下的重构精度。仿真结果表明该方法不仅可以实现多目标定位,而且具有较强的抗噪声和抗混响能力.   相似文献   

5.
传感器网络基于特征值分解的信号被动定位技术   总被引:3,自引:0,他引:3       下载免费PDF全文
郝本建  李赞  万鹏武  司江勃 《物理学报》2014,63(5):54304-054304
基于传感器网络的信号被动定位技术在电磁学、声学、声呐系统以及传热学等领域具有广泛的应用前景,当传感器网络节点所接收噪声强度不同或传输信道存在阴影衰落效应时,给出了目标信号到达距离比定位关联度量的估计方法与基于信号到达距离比的被动定位算法.将特征值分解技术引入到信号到达距离比定位关联度量估计中,通过接收信号协方差矩阵特征值分解技术估计各节点所接收噪声强度,并通过网络参考节点轮换与特征值分解方法消除阴影衰落效应所引入的定位误差,最后给出该算法的最小二乘定位解.该方法可较好的消除由于节点接收噪声强度不同以及阴影衰落效应等因素所带来的定位性能恶化.  相似文献   

6.
麦克风阵列已被广泛应用于音/视频会议等人机交互领域中时,多声源应用场景对声源方位估计性能提出了更高的要求。压缩感知(CS)声源定位算法将声源定位问题转化为信号的稀疏重构问题,相比传统的定位算法如相位变换加权(SRP-PHAT)和时延累加定位(DS)能够获得较高的定位性能,但多声源的存在一定程度上降低了稀疏程度,影响了CS重构性能。考虑到传统的CS定位算法并未利用多个连续语音帧之间声源空间向量的共同稀疏性,提出采用分布式压缩感知(DCS)理论以改善多声源的稀疏恢复估计的性能。仿真和实验结果表明,相比于传统定位算法和CS-OMP算法,DCS-SOMP算法在不同信噪比和不同声源强度的环境中,对多声源的方位估计都具有更好的定位性能和定位稳健性。  相似文献   

7.
Time Difference of Arrivals (TDOAs) of sound waves between microphones have to do with source localization. How well a sound source can be localized depends on how precisely the TDOAs are estimated. Although many ways to estimate TDOA have been proposed, noise always prevents us from finding exact time differences more or less in practice. Cross correlation has been the most prevalent way to estimate time difference, and various cross correlations robust to noise have also been developed. Nevertheless, much remains to be done for exact TDOA estimation under noisy environments. A novel way to show time delays in quefrency domain by removing noise has been proposed, which is called Minimum Variance Cepstrum (MVC). In particular, it is practically desirable to visualize source position with as few number of sensors as possible. Once TDOAs are obtained precisely, it is enough to show the source position in a 2-D plane using hyperbolic curves with only three sensors. In this work, the MVC is adopted to accurately estimate TDOAs under noise, and a way to localize an acoustic source by intersecting hyperbolic curves using the TDOAs between three microphones is proposed. Numerical simulations on TDOA estimation and source localization with white Gaussian noise demonstrated that the proposed method worked well under the noisy environment, and we compared the results with those of other old but well-established cross correlation estimators. In addition, experiments to detect a leaking point on a pipe successfully showed where the leak sound was generated.  相似文献   

8.
提出一种房间混响声场环境下的多声源二维定位跟踪算法。研究了基于盲源分离的时延估计,以及联合空间分布的多个传声器对的定位算法。用高斯似然函数解决在多源、多维情况下声源定位的时延匹对模糊问题,使之能够用双传声器对实现对多个声源的二维定位,结合粒子滤波算法实现对多个运动声源的跟踪。仿真实验验证了提出算法的有效性。  相似文献   

9.
杜博凯  曾向阳  洪汐 《应用声学》2022,41(6):901-910
对于封闭空间内的声场重放,传统的多点声压匹配方法(Cov-PM)直接依据在目标声场测得的声压直接使用最小二乘来计算扬声器权重。然而这种方法要求较多的目标声场采样点以实现足够的精度。对于一类特殊的声场景,也就是目标声场是由少量声源辐射产生时,提出一种对目标声场稀疏分解的方法来进行混响环境下多域声场重构以降低对目标声场采样数量的要求。本文给出基于目标声场等效源稀疏分解多域重构方法(Sparse-ESM)理论推导,通过数值计算以及实验测试两种方式对比所提方法与最小二乘等效源分解方法以及Cov-PM的声场重放性能。数值结果表明,在600Hz 以上的频段,Sparse-ESM 方法的重构误差性能提升明显。实验结果也得出了与数值计算相同的结论。同时,还通过数值计算和实验测试两种方法证明了当目标声场声源方向波动时,Sparse-ESM 仍然可以保持与其余两类方法相近的声对比度同时实现较高的亮区重构精度。  相似文献   

10.
A robust Capon-type algorithm is constructed for source localization by a partially calibrated array operating in an uncertain environment. Results of statistical modeling are presented to determine the accuracy of source localization and the probability of correct source detection. Experimental testing of the proposed method is carried out to demonstrate its performance in Ladoga Lake.  相似文献   

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