Manatee position estimation by passive acoustic localization

Passive sound source localization with sensor arrays is based on the estimation of the time difference of arrival (TDOA), and precise TDOA is required to achieve accurate position estimation. For a majority of practical localization systems (based on TDOA estimation with four sensors in two dimensions), only three time delays are computed to determine the location of interest. This paper presents an approach to determine the position of a manatee by using four hydrophones and all the combinations of the TDOAs available. With four hydrophones, six TDOAs are computed and then combined three by three to get 20 possible points for each position to estimate. Experimental results using the Hilbert envelope peak technique to estimate the TDOAs and the least square method to estimate the position are presented. For the tests conducted it is shown that for a manatee call having a high signal-to-noise ratio, the individual position estimated for each of the 20 combinations of TDOAs lies on a straight line, providing a good estimation of the direction of arrival approximately 85% of the time. However, a good estimation of the position is obtained for a manatee near the hydrophone array approximately 55% of the time.     

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。      

Distributed sound source localization algorithm with sound velocity calibration in windy environments

A new sound source localization method with sound speed compensation is proposed to reduce the wind influence on the performance of conventional TDOA(Time Difference of Arrival) algorithms. First, the sound speed is described as a set of functions of the unknown source location, to approximate the acoustic velocity field distribution in the wind field. Then,they are introduced into the TDOA algorithm, to construct nonlinear equations. Finally, the particle swarm optimization algorithm is used to estimate the source location. The simulation results show that the proposed algorithm can significantly improve the localization accuracy for different wind velocities, source locations and test area sizes. The experimental results show that the proposed method can reduce localization errors to about 40% of the original error in a four nodes localization system.     

Evaluation of generalized cross-correlation methods for direction of arrival estimation using two microphones in real environments

The localization of sound sources, and particularly speech, has a numerous number of applications to the industry. This has motivated a continuous effort in developing robust direction-of-arrival detection algorithms, in order to overcome the limitations imposed by real scenarios, such as multiple reflections and undesirable noise sources. Time difference of arrival-based methods, and particularly, generalized cross-correlation approaches have been widely investigated in acoustic signal processing, but there is considerable lack in the technical literature about their evaluation in real environments when only two microphones are used. In this work, four generalized cross-correlation methods for localization of speech sources with two microphones have been analyzed in different real scenarios with a stationary noise source. Furthermore, these scenarios have been acoustically characterized, in order to relate the behavior of these cross-correlation methods with the acoustic properties of noisy scenarios. The scope of this study is not only to assess the accuracy and reliability of a set of well-known localization algorithms, but also to determine how the different acoustic properties of the room under analysis have a determinant influence in the final results, by incorporating in the analysis additional factors to the reverberation time and signal-to-noise ratio. Results of this study have outlined the influence of the acoustic properties analysed in the performance of these methods.     

分布式矢量传感器对宽带源被动定位的方法研究

本文给出了两种基于矢量传感器的被动定位方法。一般来说，对目标进行被动定位可以通过方位信息或时延信息来实现。单个矢量传感器就可以实现目标方位估计，而对多个矢量传感器接收的信号进行互相关运算即可得到目标对各矢量传感器的时延信息。本文先给出了利用方位信息进行被动定位的方法，然后给出了利用分布式矢量传感器进行方位一时延信息联合被动定位的新方法，并通过仿真分析对这两种方法的定位性能进行了比较。     

An algorithm for the localization of multiple interfering sperm whales using multi-sensor time difference of arrival

In this paper an algorithm is described for the localization of individual sperm whales in situations where several near-by animals are simultaneously vocalizing. The algorithm operates on time-difference of arrival (TDOA) measurements observed at sensor pairs and assumes no prior knowledge of the TDOA-whale associations. In other words, it solves the problem of associating TDOAs to whales. The algorithm is able to resolve association disputes where a given TDOA measurement may fit to more than one position estimate and can handle spurious TDOAs. The algorithm also provides estimates of Cramer-Rao lower bound for the position estimates. The algorithm was tested with real data using TDOA estimates obtained by cross-correlating click-trains. The click-trains were generated by a separate algorithm that operated independently on each sensor to produce click-trains corresponding to a given whale and to reject click-trains from reflected propagation paths.     

Using power level difference for near field dual-microphone speech enhancement

In this contribution, a novel dual-channel speech enhancement technique is introduced. The proposed approach uses the dissimilarity between the power of received signals in the two channels as a criterion for speech enhancement and noise reduction. We claim that in near field conditions, where the distances between microphones and sound source are short, the difference in the received power levels at the two microphones is an estimate of the clean speech signal power. Then, apply this theory to present an optimum method for speech enhancement. Fortunately, the method has the ability to cope with problems such as transient noise and nearby microphones which are two of the main problems of the proposed dual-microphone speech enhancement techniques. Using objective speech quality measures and spectrogram analysis, we show that the proposed method results in improved speech quality.     

Effects of in-the-ear microphone directionality on sound direction identification

As advanced signal processing algorithms have been proposed to enhance hearing protective device (HPD) performance, it is important to determine how directional microphones might affect the localization ability of users and whether they might cause safety hazards. The effect of in-the-ear microphone directivity was assessed by measuring sound source identification of speech in the horizontal plane. Recordings of speech in quiet and in noise were made with Knowles Electronic Manikin for Acoustic Research wearing bilateral in-the-ear hearing aids with microphones having adjustable directivity (omnidirectional, cardioid, hypercardioid, supercardioid). Signals were generated from 16 locations in a circular array. Sound direction identification performance of eight normal hearing listeners and eight hearing-impaired listeners revealed that directional microphones did not degrade localization performance and actually reduced the front-back and lateral localization errors made when listening through omnidirectional microphones. The summed rms speech level for the signals entering the two ears appear to serve as a cue for making front-back discriminations when using directional microphones in the experimental setting. The results of this study show that the use of matched directional microphones when worn bilaterally do not have a negative effect on the ability to localize speech in the horizontal plane and may thus be useful in HPD design.     

传感器网络基于特征值分解的信号被动定位技术

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.     

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.     

Prediction of intake noise of an automotive engine in run-up condition

It is very important to predict the radiated noise from the engine intake system for the effective noise control and virtual prototyping of in-cavity and outdoor noise of a vehicle. To this end, one should precisely measure the in-duct acoustic source parameters of the intake system, viz., source strength and source impedance. Usually, the noise radiation characteristics need to be expressed as a function of engine speed. In this study, acoustic source parameters of an engine intake system under engine run-up condition were measured by using the direct method. Direct method employed two external loudspeakers, turned on simultaneously, and three microphones for the separation of upstream and downstream wave components. It was noted that the frequency spectra of source impedance hardly changes with the increase of engine speed. Utilizing this fact, source strength under the engine run-up condition was calculated by assuming invariant source impedance. Predicted insertion loss and radiated sound pressure level using the measured source parameters were compared with those of measured data and predicted data using several idealized source models, which have been adopted for the calculations. A reasonably good agreement was observed between measured sound spectra at the intake orifice and predicted one using the measured source data. It was shown that the source data obtained by the present method yielded a far better prediction accuracy than those by the idealized source models.     

A miniaturized adaptive microphone array under directional constraint utilizing aggregated microphones

This paper introduces a miniaturized microphone array using the Directionally Constrained Minimization of Power (DCMP) method, which utilizes the transfer functions of microphones located at the same place, namely aggregated microphones. The phased microphone array realizes a noise reduction and direction of arrival (DOA) estimation system according to differences in the arrival time, phase shift, and/or the level of the sound wave for each microphone. Hence it is difficult to miniaturize the microphone array. The objective of our research is to miniaturize the system size using aggregated microphones. In this paper, we first show that the phased microphone array system and the proposed aggregated microphone system can be described within the same framework. We then apply a microphone array under directional constraint to the aggregated microphones and compare the proposed method with the microphone array. We show the directional pattern of the aggregated microphones. We also show the experimental results regarding DOA estimation.     

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°.     

Blind extraction and localization of sound sources using point sources based approaches

This paper presents theoretical models for blind sound source localization and separation of the signals emitted by arbitrary point sources in free space. Source localizations are achieved by a model based approach that accounts for the spherical spreading of an acoustic wave and utilizes an iterative triangulation, based on the signals measured by a three-dimensional microphone array. Once source locations are determined, the source signals are separated by using the point source separation (PSS) method, which is valid for all types of signals, including harmonic, continuous, transient, random, narrowband and broadband. General solutions for signals separation are presented. Theoretically, PSS can reconstruct the individual source signals exactly. This is because it employs the free-space Green's function, which defines the exact correlation among individual sources and measurement microphones. To validate PSS, numerical simulations are carried out and results are compared with those obtained by FastICA (Independent Component Analysis) code. The impacts of various parameters such as the microphone configuration, type of source signals, signal to noise ratio, number of microphones and source localization errors on the quality of signals separation by using PSS and FastICA are examined. The advantages and disadvantages of PSS and FastICA are compared and discussed.     

Feedback control of sound transmission through a double glazed window

One way to tackle the control of stochastic noise in three dimensions is to reduce the sound transmission to the zone of interest. In buildings, windows are often the weak link in protecting the interior from outside noise. In particular, double glazed windows have a poor sound insulation at low frequency around the mass-air-mass resonance (double wall resonance). Since passive means for windows are exhausted, an active controller that increases the transmission loss in the low-frequency range is an attractive approach to reduce the noise level in buildings. Previously suggested feedforward controllers need reference microphones to measure the disturbance outside and error microphones for the adaptation somewhere in the room. For a real window this is unpractical or even unfeasible. These limitations can be overcome with the feedback controller presented here, which only uses sensors and actuators in the cavity of the double glazed window. Four different controllers—two feedforward and two feedback strategies—are designed, implemented and compared. With feedback the noise transmission around the mass-air-mass resonance can be reduced by , compared to with a feedforward controller.     

吸顶式传声器阵列阵元坐标标定方法

提出了一种吸顶式传声器阵列阵元坐标的标定方法。针对在混响声场中,时延估计算法性能严重下降从而导致在标定传声器阵元坐标时产生较大误差的问题,提出了利用脉冲声源作为标定声源,并且截取脉冲源直达声的方法来抑制混响声场的影响,提高传声器阵元坐标标定的精度。建立了阵元坐标标定的误差分析模型,并以白噪声和脉冲声源作为标定声源进行数据仿真和对比分析。仿真结果表明,使用脉冲声源作为标定声源能有效地抑制混响声场的影响,获得传声器阵列阵元的准确坐标。同时,在封闭的房间内建立起孔径为3.5 m、64阵元的螺旋状吸顶传声器阵列进行了实验研究,实验结果验证了本文提出方法的有效性。      

拖曳阵被动合成孔径目标深度稳健估计

使用水下无人平台作为载体的拖曳阵进行被动目标深度估计具有灵活性高和隐蔽性好的优点,针对实际应用中存在的平台自噪声和阵列瞬时随机加速度扰动问题,提出了一种稳健的目标深度估计方法。该方法分为三个步骤,首先对阵元接收信号进行自适应噪声抵消和相位抖动滤波,然后对声压进行距离积分实现简正波模态估计,最后计算模态匹配度,最大值对应的深度为目标深度估计结果。仿真表明在干扰背景下该方法的目标深度估计稳健性优于传统方法,声源频率、合成孔径距离和信干比决定了目标深度估计误差。利用实验数据验证了该方法对水下低频线谱声源的深度估计能力。     

格林函数解卷积处理的阵不变量浅海无源定位

水下声源无源定位是声呐技术重要的研究方向.针对水下声源无源定位问题,本文提出了一种基于格林函数解卷积处理的阵不变量无源定位方法.该方法使用盲解卷积算法从水平阵接收信号中提取时域格林函数,然后采用空域解卷积方法处理得到的时域格林函数,获得波束时间偏移,从波束时间偏移中计算得到阵不变量,解算目标距离,从而实现声源定位.区别...