基于粒子滤波的声源定位方法

针对噪声与混响环境下的声源定位问题,采用了一种基于粒子滤波的麦克风阵列的声源定位方法。在粒子滤波框架下,将到达麦克风的语音信号作为观测信息,通过计算麦克风阵列波束形成器的输出能量来构建似然函数。实验结果表明,方法提高了声源定位系统的抗噪声与抗混响能力,即使在低信噪比强混响的环境下也能获得较高的定位精度。     

基于麦克风阵列的声源定位研究

以基于声达时间差(TDOA)的定位技术为基础,在噪声和混响同时存在的环境下,对基于麦克风阵列的声源定位方法进行了系统研究。在传统LMS自适应算法的基础上,提出了一种基于语音激励信息的LMS自适应时延估计新方法,再结合平面四元几何法定位。经过模拟房间环境的实验验证,该方法抗噪声、抗混响能力强,是一种定位精度高,运算量小的声源定位方法,可用于实时定位。     

基于加权预测误差算法的声源定位方法

混响是导致室内声源定位精度下降的主要因素之一.为了降低混响环境下的定位误差,本文应用加权预测误差算法对麦克风阵列信号进行去混响预处理.为了定量分析该去混响方法为声源定位带来的精准度上的提升,我们在多种混响时间、多种白噪声信噪比、两类定位算法和两种阵列参数条件下进行了仿真和实验.仿真和实验结果均表明,与不使用去混响预处理...     

基于粒子滤波的麦克风阵列声源定位方法

针对噪声与混响环境下的声源定位问题,本文采用了一种基于粒子滤波的麦克风对的声源定位方法。该方法在粒子滤波框架下,将到达麦克风对的时间差作为观测信息,通过计算麦克风对的广义互相关函数(GCCF)来构建似然函数。实验结果表明,本文所采用的方法提高了声源定位系统的抗噪声与抗混响能力,即使在低信噪比强混响的环境下也能获得较高的定位精度。     

利用块稀疏特性的压缩感知麦克风阵列声源定位

与传统声源定位算法如相位变换加权、时延累加定位不同,压缩感知麦克风阵列声源定位算法可将声源定位转化为稀疏重构问题从而获得较高的性能。但在实际应用环境下,由于远场声源自身指向性、空间混响等原因,声源方向向量往往呈现块稀疏度结构,导致采用传统稀疏恢复算法如正交匹配追踪算法（Orthogonal matching pursuit,OMP）等进行压缩感知定位性能下降。本文在压缩感知声源定位算法中引入块稀疏似零范数,以压缩感知为基本框架,采用块稀疏似零范数稀疏恢复进行声源方向向量的重构,获取声源的方位。实验结果表明,相较于传统声源定位算法和基于OMP的压缩感知声源定位算法,本文算法具有更高的定位精度。     

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。     

基于麦克风阵列的真实声场环境声源定位跟踪系统设计

设计制作了基于麦克风阵列的真实声场环境声源定位跟踪系统。该系统基于树莓派4B和Arduno单片机，依托声阵列云台，通过传感器采集声音信号，采用TDOA算法对信号进行处理，集采集、处理、控制于一体，成功实现对目标的定位与跟踪。采用两种独立的信号处理方法：以TDOA算法为主，幅度相位检测为辅，对结果进行验证和自检，形成闭环正反馈，增强了该定位系统的准确性和稳定性。另外，针对声源存在回音、噪声干扰等问题，将麦克风阵列变全向为单向以及引入一个基于相关运算的语音检测算法，提高了定位系统的抗噪声能力。测试表明在声场环境下该系统能够对单个声源的三维空间位置进行实时的定位和跟踪，当声源位置变化时，系统也能较为准确跟踪声源的位置。     

基于TMS320DM642麦克风阵列声源定位系统

麦克风声源定位是利用麦克风阵列拾取语音信号,并用数字信号处理技术对其进行分析和处理的声源定位技术.在麦克风阵列声源定位中,语音信号端点的拾取是重要的环节.语音端点检测是对接收到的信号利用端点检测算法分析,以确认麦克风阵列中语音信号到达的端点;并利用麦克风阵列中各麦克风接收到的语音信号的端点的先后,计算出麦克风阵列接收的...     

麦克风阵列语音增强技术及其应用

本文简要叙述了应用麦克风阵列进行语音增强的原理及方法。且由于麦克风阵列在实际语音处理时具有良好的拾取语音能力及噪声鲁棒性,本文将介绍该技术在车载系统环境、机器人语音识别、大型场所的记录会议、助听装置及声源定位等系统中的应用。     

针对头佩式麦克风阵列的声源定位算法研究

头佩式麦克风阵列在单兵便携反狙击声探测定位系统和机器人声定位系统中具有实际的应用价值。一般的声源定位方法是基于无遮挡的线性或非线性麦克风阵列。采用头佩式麦克风阵列,考虑到背向声源麦克风的低频声波由于头盔遮挡而发生的衍射作用,针对低频波段的声音信号进行定位算法的设计和研究。该算法利用低频声波的绕射路径计算时延,采用联合可控功率响应（SRP-PHAT）框架进行时延补偿搜索定位。实验表明,相比于普通的无遮挡定位算法,基于绕射路径的头佩式麦克风阵列定位方法通过综合利用背向声源的麦克风数据,明显地提高了定位的精度,这种精度的提升在选择1 kHz以内的信号频率窗口时达到最佳效果。     

麦克风阵列下互相关函数分类的声源定位

传统的基于麦克风阵列的声源定位方法,往往容易受到低信噪比或高混响等不利的声学条件的影响。近年来,基于模式识别和机器学习技术的方法被用来在恶劣环境下进行声源定位。引入了一种基于Fisher判别理论的加权方法,实现了基于Fisher加权朴素贝叶斯分类器（Fisher Weighted Naive Bayes Classifier,FWNBC）的声源定位。通过基于相位变换（Phase Transformation,PHAT）加权的互相关函数来计算每个位置的特征向量,利用Fisher加权朴素贝叶斯分类器估计声源位置。在实际的定位系统中进行实验,验证改进算法的性能。实验结果表明,与使用朴素贝叶斯分类器（Naive Bayes Classifier,NBC）相比,FWNBC算法有效提高了声源定位的精度。     

混响环境下基于倒谱BRIR的双耳互相关声源定位算法

在实际封闭环境中,针对存在混响而导致声源定位性能下降的问题,提出一种基于倒谱双耳房间脉冲响应（Binaural room impulse response,BRIR）的双耳互相关声源定位方法.该方法通过从倒谱BRIR中减去混响分量,然后反变换到时域得到估计的脉冲响应,再与数据库中的头部脉冲响应（Head related impulse response,HRIR）进行互相关运算,最大互相关值相对应的位置就是所估计的声源位置.仿真实验结果表明,提出的算法能减少混响环境中带来的定位误差,提高声源定位的精度.     

Enhanced sound localization

A new approach to sound localization, known as enhanced sound localization, is introduced, offering two major benefits over state-of-the-art algorithms. First, higher localization accuracy can be achieved compared to existing methods. Second, an estimate of the source orientation is obtained jointly, as a consequence of the proposed sound localization technique. The orientation estimates and improved localizations are a result of explicitly modeling the various factors that affect a microphone's level of access to different spatial positions and orientations in an acoustic environment. Three primary factors are accounted for, namely the source directivity, microphone directivity, and source-microphone distances. Using this model of the acoustic environment, several different enhanced sound localization algorithms are derived. Experiments are carried out in a real environment whose reverberation time is 0.1 seconds, with the average microphone SNR ranging between 10-20 dB. Using a 24-element microphone array, a weighted version of the SRP-PHAT algorithm is found to give an average localization error of 13.7 cm with 3.7% anomalies, compared to 14.7 cm and 7.8% anomalies with the standard SRP-PHAT technique.     

Priority-Driven Acoustic Modeling for Virtual Environments

Geometric acoustic modeling systems spatialize sounds according to reverberation paths from a sound source to a receiver to give an auditory impression of a virtual 3D environment. These systems are useful for concert hall design, teleconferencing, training and simulation, and interactive virtual environments. In many cases, such as in an interactive walkthrough program, the reverberation paths must be updated within strict timing constraints - e.g., as the sound receiver moves under interactive control by a user. In this paper, we describe a geometric acoustic modeling algorithm that uses a priority queue to trace polyhedral beams representing reverberation paths in best-first order up to some termination criteria (e.g., expired time-slice). The advantage of this algorithm is that it is more likely to find the highest priority reverberation paths within a fixed time-slice, avoiding many geometric computations for lower-priority beams. Yet, there is overhead in computing priorities and managing the priority queue. The focus of this paper is to study the trade-offs of the priority-driven beam tracing algorithm with different priority functions. During experiments computing reverberation paths between a source and a receiver in a 3D building environment, we find that priority functions incorporating more accurate estimates of source-to-receiver path length are more likely to find early reverberation paths useful for spatialization, especially in situations where the source and receiver cannot reach each other through trivial reverberation paths. However, when receivers are added to the environment such that it becomes more densely and evenly populated, this advantage diminishes.     

Joint Dereverberation and Residual Echo Suppression of Speech Signals in Noisy Environments

Hands-free devices are often used in a noisy and reverberant environment. Therefore, the received microphone signal does not only contain the desired near-end speech signal but also interferences such as room reverberation that is caused by the near-end source, background noise and a far-end echo signal that results from the acoustic coupling between the loudspeaker and the microphone. These interferences degrade the fidelity and intelligibility of near-end speech. In the last two decades, postfilters have been developed that can be used in conjunction with a single microphone acoustic echo canceller to enhance the near-end speech. In previous works, spectral enhancement techniques have been used to suppress residual echo and background noise for single microphone acoustic echo cancellers. However, dereverberation of the near-end speech was not addressed in this context. Recently, practically feasible spectral enhancement techniques to suppress reverberation have emerged. In this paper, we derive a novel spectral variance estimator for the late reverberation of the near-end speech. Residual echo will be present at the output of the acoustic echo canceller when the acoustic echo path cannot be completely modeled by the adaptive filter. A spectral variance estimator for the so-called late residual echo that results from the deficient length of the adaptive filter is derived. Both estimators are based on a statistical reverberation model. The model parameters depend on the reverberation time of the room, which can be obtained using the estimated acoustic echo path. A novel postfilter is developed which suppresses late reverberation of the near-end speech, residual echo and background noise, and maintains a constant residual background noise level. Experimental results demonstrate the beneficial use of the developed system for reducing reverberation, residual echo, and background noise.      

Dereverberation and Denoising Using Multichannel Linear Prediction

Reverberation in a room severely degrades the characteristics and auditory quality of speech captured by distant microphones, thus posing a severe problem for many speech applications. Several dereverberation techniques have been proposed with a view to solving this problem. There are, however, few reports of dereverberation methods working under noisy conditions. In this paper, we propose an extension of a dereverberation algorithm based on multichannel linear prediction that achieves both the dereverberation and noise reduction of speech in an acoustic environment with a colored noise source. The method consists of two steps. First, the speech residual is estimated from the observed signals by employing multichannel linear prediction. When we use a microphone array, and assume, roughly speaking, that one of the microphones is closer to the speaker than the noise source, the speech residual is unaffected by the room reverberation or the noise. However, the residual is degraded because linear prediction removes an average of the speech characteristics. In a second step, the average of the speech characteristics is estimated and used to recover the speech. Simulations were conducted for a reverberation time of 0.5 s and an input signal-to-noise ratio of 0 dB. With the proposed method, the reverberation was suppressed by more than 20 dB and the noise level reduced to -18 dB.     

一种改进的分数时延估计方法及应用

传统的分数时延估计算法对环境噪声和混响噪声比较敏感,在复杂的实际环境中,算法性能会严重下降。为进一步提高时延估计算法性能,提出一种基于广义互相关（Generalized cross correlation,GCC)改进算法的广义互相关 最大似然相位补偿（ GCC Maximum likelihood phase compensation,GCC MLP)分数延时估计算法。该算法改进了GCC频域加权函数,并将线性相位补偿应用于频域互相关谱,获得连续的分数时延估计值,进一步提高了分数时延估计的精确性。仿真结果表明,GCC MLP相位补偿分数时延估计算法增强了对环境噪声和混响噪声的鲁棒性,减小了时延估计误差,算法性能优于曲线拟合、Sinc插值等传统分数时延估计算法。     

Near field acoustic localization under unfavorable conditions using feedforward neural network for processing time difference of arrival

Using time difference of arrival (TDOA) is one of the two approaches that utilize time delay for acoustic source localization. Combining the obtained TDOAs together with geometrical relationships within acoustic components results in a system of hyperbolic equations. Solving these hyperbolic equations is not a trivial procedure especially in the case of a large number of microphones. The solution is additionally compounded by uncertainties of different backgrounds. The paper investigates the performance of neural networks in modelling a hyperbolic positioning problem using a feedforward neural network as a representative. For experimental purposes, more than 2000 sound files were recorded by 8 spatially disposed microphones, for as many arbitrarily chosen acoustic source positions. The samples were corrupted by high level correlated noise and reverberation. Using cross-correlation, with previous signal pre-processing, TDOAs were evaluated for every pair of microphones. On the basis of the obtained TDOAs and accurate sound source positions, the neural network was trained to perform sound source localization. The performance was examined using a large number of samples in terms of different acoustic sensors setups, network configurations and training parameters. The experiment provided useful guidelines for the practical implementation of feedforward neural networks in the near-field acoustic localization. The procedure does not require substantial knowledge of signal processing and that is why it is suitable for a broad range of users.     

基于时差法的复杂转子系统声发射源定位算法

在复杂转子系统的碰摩声发射源定位中,常规的广义互相关时延估计算法难以得到准确的时延值,进而无法进行准确定位。针对这一问题,引入基于平滑相干变换(Smoothed Coherence Transform, SCOT)的双加权二次互相关时延估计算法,计算声发射源信号的到达时间差;再利用Hilbere差值法对相关峰值进行锐化,减小在碰摩过程中噪声的干扰,以获得较为精确的时延估计结果;最后使用得到的时延值进行声发射源定位。实验结果表明,与常规的广义互相关算法相比,该算法对复杂转子系统碰摩声发射源定位具有较高的定位精度。