差分传声器阵列期望最大化多声源方位估计方法

针对小尺寸传声器阵列多声源方位估计易受混响噪声影响的问题,提出了一种适用于差分传声器阵列的期望最大化多声源方位估计方法。首先,该方法利用期望最大化算法求解出各个时频点瞬时方位估计所应满足的高斯混合模型参数;然后,通过时频点分离技术估计出各声源的方位值。针对现有的硬、软时频点分离技术应用于差分传声器阵列所存在的缺陷,还提出了一种改进的时频点分离方法,该方法融合了软、硬分离方法所具有的优点,有效降低了时频点分离结果对混响噪声的敏感性。仿真和实测实验结果表明:相较于现有的差分传声器阵列多声源估计方法,所提方法在混响噪声环境下具有更高的估计精度和稳健性能。      

新型声学超材料单通道麦克风可实现多声源实时定位与分离

在人工系统中,科研人员通常借助由多个传声器组成的传声器阵列来解决声源定位和分离问题。具有高精度声源定位和分离能力的传声器阵列往往需要较大的阵元数量和物理尺寸,这种阵列系统不仅不便于安装和操控,处理多通道信号的计算成本往往也很大,从而导致其应用受限。受生物听音机制的启发,中国科学院噪声与振动重点实验室的博士生孙雪聪与其导师杨军研究员、贾晗研究员等提出了一种基于声学超材料的单通道多声源的定位与分离系统,用一个带有超材料外壳的单通道麦克风实现了三维空间中多个同时发声声源的实时定位与分离。     

基于声传播算子的声源定位实验模拟方法研究

声传播算子是一种高效的时域声场计算方法,它能够很方便地计算出给定系统参数下任意时刻任意位置的声场变化情况,本文采用这种方法计算所得的二维房间声场信息进行传声器阵列的声源定位仿真实验。计算结果表明,用该方法获取的阵列数据能有效地应用于阵列信号处理算法中,准确地估计出初始高斯脉冲声源的方向。声传播算子声场计算方法能为传声器阵列声源定位的实验提供方便,使得传声器阵列声源定位算法在不同混响时间的鲁棒性实验研究变得更加简捷。     

分布式压缩感知麦克风阵列多声源方位估计*

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。     

基于波束形成的多类型多声源定位研究

为实现空压机多噪声源的准确定位,仿真对比了多种近场球面波多声源定位算法。基于时域波束形成,研究了相同声源平面、不同声源频率、不同声源纵向距离、不同声源强度下多声源定位以及声源频率、声源纵向距离和声源强度多因素联合的多声源定位仿真方法,模拟了更接近实际的噪声源类型。基于频域波束形成,仿真研究了1400 Hz,2400 Hz,3400 Hz,4400 Hz的多声源。分别利用互功率谱波束形成和除自谱的互功率谱波束形成,仿真研究了相干声源和不相干声源。开发了阵列声成像测试平台,运用频域波束形成和功率谱波束形成对空压机进行了定位试验研究。结果表明,1400 Hz下空压机的主要噪声源是气缸盖、空气滤清器和曲轴附近的机体,这可为空压机减振降噪改进设计提供依据。     

阵元随机均匀分布球面阵列联合噪声源定位方法

基于球面传声器阵列的噪声源定位方法,设计加工了阵元随机均匀分布64元球面传声器阵列,研究了球面近场声全息和球谐函数模态展开聚焦波束形成联合噪声源定位识别方法,对算法的性能进行了仿真分析,并利用球面传声器阵列进行了噪声源的定位识别试验.研究表明,阵元随机均匀分布球面阵列具有全空间稳定的目标定位性能,球面近场声全息对低频近距离声源具有较高的定位精度,球谐函数模态展开聚焦波束形成对高频远距离声源具有较高的定位精度,将两种方法联合进行声源的定位识别,可以在较小孔径的球面阵列和较少阵元的条件下,在宽频带范围内获得对目标声源良好的定位性能.     

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。     

车载场景结合盲源分离与多说话人状态判决的语音抽取

在车载分布式传声器阵列场景中,结合盲源分离TRINICON (Triple-N ICA for convolutive mixtures)算法与多说话人状态判决实现期望语音抽取。根据分布式传声器阵列与声源的相对位置关系,设计特定的盲源分离初始化条件以保证输出通道与声源的映射关系;根据分布式传声器阵列的频响特点,设计特征矢量来进行多说话人判决,并将判决结果引入TRINICON算法参数迭代过程。在使用实际车载录音数据的仿真评测中,所提方法在不同信噪比下有较高的鲁棒性,可有效提升TRINICON算法的收敛速度和语音信号的信扰比,且可以确保准确的通道映射。评测结果表明该方法可以在车载场景中有效抽取出期望语音,为车载复杂场景下的声信息提取提供了一种可靠且收敛快速的解决方法。      

采用听觉滤波器的宽带MUSIC声源定位方法

在分析了采用短时傅里叶变换的宽带MUSIC声源定位算法(SF-MUSIC)存在问题的基础上,提出了一种采用听觉滤波器的宽带MUSIC声源定位算法(AF-MUSIC)。该算法使用听觉滤波器组对传声器阵列接收到的信号进行不等带宽分解后,在各个频率通道上使用MUSIC算法进行声源定位,并结合子区间频数估计法得出最终定位结果。对算法进行的实验评估表明,在不同声源类型条件下,相比SF-MUSIC算法,AF-MUSIC算法的平均估计误差减少2.5479°,有效地提高了声源波达方向估计的精度。      

基于双传声器对的多声源二维定位跟踪算法

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。      

一种多基阵机动目标被动跟踪算法

由多部声纳基阵获取的方位信息对水中机动目标的跟踪实质上是一个非线性状态估计问题,文中首先依据各基阵的方位信息,采用最小二乘法得到目标位置在各采样时刻的初步估计,然后将其作为测量值用于交互多模型算法(IMM)并结合线性卡尔曼滤波(KF)得到目标运动速度和轨迹,避免了应用非线性估计算法直接进行多个方位数据融合过程中存在的各种问题。仿真结果表明这一算法简便,与双基阵纯方位机动目标被动跟踪相比具有较快的收敛速度和较高的跟踪精度。     

Low latency localization of multiple sound sources in reverberant environments

Sound source localization algorithms determine the physical position of a sound source in respect to a listener. For practical applications, a localization algorithm design has to take into account real world conditions like multiple active sources, reverberation, and noise. The application can impose additional constraints on the algorithm, e.g., a requirement for low latency. This work defines the most important constraints for practical applications, introduces an algorithm, which tries to fulfill all requirements as good as possible, and compares it to state-of-the-art sound source localization approaches.     

The influence of environmental conditions on estimation of source distance and height using a single vertical array

The performance of microphone arrays outdoors is influenced by the environmental conditions. Numerical simulations indicate that, while horizontal arrays are hardly affected, direction-of-arrival (DOA) estimation with vertical arrays becomes biased in presence of ground reflections and sound speed gradients. Turbulence leads to a huge variability in the estimates by reducing the ground effect. Ground effect can be exploited by combining classical source localization with an appropriate propagation model (ground effect inversion). Not only does this allow the source elevation and range to be determined with a single vertical array but also it allows separation of sources which can no longer be distinguished by far field localization methods. Furthermore, simulations provide detail of the achievable spatial resolution depending on frequency range, array size and localization algorithm and show a clear advantage of broadband processing. Outdoor measurements with one or two sources confirm the results of the numerical simulations.     

A localization approach for multiple sound sources via an expectation maximization algorithm using differential microphone arrays

Conventional sound localization approaches with small-sized microphone arrays are usually sensitive to noise and reverberation. To deal with the problem, an approach based on expectation maximization algorithm with differential microphone arrays(DMAs) is proposed.Firstly, the parameters of Gaussian mixture model for time-frequency instantaneous direction estimation are estimated through the EM algorithm, and then the direction of each sound source is estimated via time-frequency separation. In order to overcome the weakness of existing time-frequency separation techniques, an improved method, which combines the advantages of both the hard and soft separation methods, is also proposed. The improved time-frequency separation method is shown to be less sensitive to noise and reverberation. Simulation and experimental results demonstrate that the proposed localization approach is superior to its existing counterparts in terms of localization accuracy and robustness.     

Estimation of multiple sound source directions using artificial robot ears

Estimating the direction of a sound source is an important technique used in various engineering fields, including intelligent robots and surveillance systems. In a household where a user’s voice and noises emitted from electric appliances originate from arbitrary directions in 3-D space, robots need to recognize the directions of multiple sound sources in order to effectively interact with the user.This paper proposes an ear-based estimation (localization) system using two artificial robot ears, each consisting of a spiral-shaped pinna and two microphones, for application in humanoid robots. Four microphones are asymmetrically placed on the left and right sides of the head. The proposed localization algorithm is based on a spatially mapped generalized cross-correlation function which is transformed from the time domain to the space domain by using a measured inter-channel time difference map. For validation of the proposed localization method, two experiments (single- and multiple-source cases) were conducted using male speech. In the case of a single source, with the exception of laterally biased sources, the localization was achieved with an error of less than 10°. In a multiple-source environment, one source was fixed at the front side and the other source changed its direction; from the experimental results, the error rates on the localization of the fixed and varying sources are 0% and 36.9% respectively within an error bound of 15°.     

Robust tracking of moving sound source using multiple model Kalman filter

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.