Source Localization System Research Articles

Design and Development of Embedded Sound Source Localization System

The system is based on ARM as the core of the embedded platform, using STM32F103 chip, comprehensively designing adjustable gain microphone, AD7606 eight channel synchronous acquisition chip,W5300 Ethernet chip, Qt upper computer and so on to complete a multi-channel sound source localization system integrating acquisition, storage, upload, analysis and display of sound signals. The system use STM32F103 chip to control the core and source localization algorithm based on time delay estimation of arrival time difference with small computation, accurate localization and good real-time performance. Through the design and fabrication of microphone array, the acquisition and pre-processing function of sound signal is accomplished by using eight-channel synchronous acquisition module. The data is transmitted to the upper computer by using Ethernet module, and the upper computer performs algorithm processing and display. The system is simple in structure, small in size and easy to use. The test results show that the system can not only acquire complete sound data and high waveform reduction, but also display real-time sound source waveform in the upper computer. The positioning result is ideal, high accuracy, good real-time, and has certain practical value.

Implementation of Sound Source Localization System Based on LabVIEW

This paper uses LabVIEW as a software platform combined with the PXIe-4497 sound and vibration acquisition card to design a sound source localization system. Using the sounding device as sound source, the data is received by the microphone array. After the processing of the sound source localization system, the sound image is added with the video to achieve the locating of the sound source. The system consists of three modules: sound pressure data acquisition module, video acquisition module and data processing module. The sound pressure data acquisition module is programmed by using the DAQmx API function, the video acquisition module is based on the IMAQdx function, and the data processing module uses the phase-shift sum beamforming algorithm. The design of this system can provide practical references for future applications such as noise detections.

Određivanje pozicije akustičkih izvora korišćenjem spektralne analize višeg reda za određivanje vremena nailaska signala

The localization of various acoustic sources in a battlefield (such as weapon rounds, mortars, rockets, mines, improvised explosive devices, vehicle-borne improvised explosive devices and airborne vehicles) nowadays has a significant history. In acoustic source localization systems of multiple sensors, networked and placed at known positions are used to detect signals emitted from the source and perform localization of the source. Time of arrival estimation of the gun fire shock waves at large distances is significant when the purpose is the gun localization. At large distances shock wave signal is significantly deformed and therefore it is relatively difficult to accurately estimate the time of arrival. In this paper methods for TOA estimation are proposed, which are based on cumulants of the acoustic signal which originate from distant gunfire events. Localization of acoustic sources is performed in two steps by time difference estimation of acoustic signal arrival and using Discrete Probability Density method for positioning. Numerous field experiments have been conducted in order to verify performance of the proposed approach.

TDOA estimation of dual-satellites interference localization based on blind separation

The time difference of arrival (TDOA) estimation plays a crucial role in the accurate localization of the satellite interference source. In the dual-satellites interference source localization system, the target signal from the adjacent satellite is likely to be interfered by the normal communication signal with the same frequency. Therefore, the signal to noise ratio (SNR) of the target signal would become too low, and the TDOA estimation through cross-correlation processing would be unreliable or even unattainable. This paper proposes a technique based on blind separation to solve the co-channel interference problem, where separation of the mixed signal can be carried out by the particle filter (PF) algorithm. The experimental results show that the proposed method could achieve more accurate TDOA estimation. The measured data obtained by using the software radio platform at 915 MHz and 2 GHz respectively verify the effectiveness of the proposed method.

A 2-D Acoustic Source Localization System for Drones in Search and Rescue Missions

Search and rescue missions still typically rely on teams of people tirelessly searching for victims. Advances in technology have seen the use of drones to help the rescuers in their search. These drones primarily use visual systems, but cost and visibility are major drawbacks with those systems. The system described in this paper attempts to resolve the localization problem for drones used in search and rescue operations by making use of acoustic source localization. The source location is estimated in terms of an azimuth estimation (the bearings or angle toward the source) as well as the distance. The proposed system is shown the promising results for both the azimuth and distance estimations.

Development of the application of the digital microphone array system for real time source localization

This research is to develop a digital microphone array system to build a real-time sound source localization system and to produce acoustical holography. Acoustical holography can be understood as taking a photo of sound which can be used to source localization. In the situation of free field with an unknown signal, source signal is restored by time reversal mirror(TRM) where a free-field impulse response function(IRF) is used to achieve the goal of real time source localization. During the processing, the unknown source location is identified by scanning over a possible space for source locations to find a maximum restored signal. In the present study, simulations and experiments of source localization are carried out in 2D and 3D space. In the experiment of source localization with single source, the estimation errors are below 10% in certain distances between source and microphone array. For two sources situation, the distance between two sources will limit the application of the present approach.

Acoustic Emission Source Localization System Using Fiber Bragg Grating Sensors and a Barycentric Coordinate-Based Algorithm

Structures and machines are very susceptible to some barely visible defects; acoustic emission (AE) is an effective technique of detecting defects and examining growth and location of these defects. In this paper, a new method was proposed to predict the position of the AE source. The dynamic strain signal from the AE source was acquired by fiber Bragg grating (FBG) sensors. Complex Morlet wavelet transform was used to extract narrowband signals from AE waves. It was proposed that the barycentric coordinate-based localization algorithm can be used to predict the coordinates of the AE source. The validation tests based on the designed AE detection system were performed on a 500 mm × 500 mm × 2 mm Al-alloy plate. The experimental results show that the proposed method is feasible.

Real-time source localization using phase and amplitude gradient estimator for acoustic intensity

Recent demonstrations of real-time source localization systems have used a variety of direction-finding methods. One such system uses a multi-microphone probe to estimate acoustic intensity [Inoue et al., Proc. Meetings Acoust. 29, 025001 (2016)], although the bandwidth of intensity estimates are traditionally limited by the microphone spacing. The Phase and Amplitude Gradient Estimator (PAGE) method [Thomas et al., J. Acoust. Soc. Am. 137, 3366–3376 (2015)] greatly extends the bandwidth of active intensity, providing an accurate method of source localization for a wide bandwidth. An initial system implemented in LabVIEW estimates active intensity using the PAGE method to identify the direction of the source location in real time. Using a paired webcam, the program then overlays the direction as a three-dimensional arrow onto a webcam image. A demonstration will be provided. Initial quantitative results show the accuracy of the system and limitations of the device. [Sponsored by the NSF REU program.]

Error Analysis of Sound Source Localization System for Small Microphone Based on Time Delay Estimation

Based on time delay,Sound source localization method has a smaller amount of calculation compared with the sound source localization method of controllable beam synthesizer and high resolution spectral estimation, and can achieve a higher positioning accuracy after improved, so it is the most suitable method for real-time positioning, widely used in the field of passive acoustic localization. This paper tries to spread the factors that affect the positioning accuracy of time delay method, and provides the direction for improving the effect of time delay sound source localization.

Design and Assessment of Sound Source Localization System with a UAV-Embedded Microphone Array

[abstFig src='/00290001/15.jpg' width='300' text='Visualization of localization result' ] We have studied on robot-audition-based sound source localization using a microphone array embedded on a UAV (unmanned aerial vehicle) to locate people who need assistance in a disaster-stricken area. A localization method with high robustness against noise and a small calculation cost have been proposed to solve a problem specific to the outdoor sound environment. In this paper, the proposed method is extended for practical use, a system based on the method is designed and implemented, and results of sound source localization conducted in the actual outdoor environment are shown. First, a 2.5-dimensional sound source localization method, which is a two-dimensional sound source localization plus distance estimation, is proposed. Then, the offline sound source localization system is structured using the proposed method, and the accuracy of the localization results is evaluated and discussed. As a result, the usability of the proposed extended method and newly developed three-dimensional visualization tool is confirmed, and a change in the detection accuracy for different types or distances of the sound source is found. Next, the sound source localization is conducted in real-time by extending the offline system to online to ensure that the detection performance of the offline system is kept in the online system. Moreover, the relationship between the parameters and detection accuracy is evaluated to localize only a target sound source. As a result, indices to determine an appropriate threshold are obtained and localization of a target sound source is realized at a designated accuracy.

Određivanje pozicije akustičnih izvora korišćenjem metode diskretne raspodele verovatnoća podržane združenom detekcijom i procenom vremena nailaska signala

The localization of various acoustic sources in a battlefield (such as weapon rounds, mortars, rockets, mines, improvised explosive devices, vehicle-borne improvised explosive devices and airborne vehicles) nowadays has significant history. In acoustic source localization systems multiple sensors (such as microphones or microphone arrays), placed at known positions and networked, are used to detect signals emitted from the source and perform localization of the source. In this paper, Discrete Probability Density (DPD) method, as a method for position determination, has been used to estimate location of acoustic sources such as artillery weapons. Wavelet decomposition of the acoustic signal enables to emphasize N-wave in the time domain. Time of Arrival (TOA) estimation is realized by a statistical analysis of narrow time segment which hold N-wave. Based on the estimated TOA Time Difference of Arrival (TDOA) is calculated. TDOA presents relative time difference of arrival of the sound signal between pairs of sensors. Localization of the acoustic sources is performed by time difference estimation of acoustic signal arrivals using Discrete Probability Density method. A number of experiments with fire off mortar projectiles have been conducted in order to verify the performance of the proposed approach.

複数フレームのCSP係数を用いたTDOA推定による複数人物定位·追跡

In this paper, multi-speaker localization methods using voice signal are proposed. Sound source localization systems consisting of many microphones have been widely researched. In our laboratory, we have researched sound source localization of persons based on TDOA (Time Difference of Arrival) between distributed microphones for the purpose of efficient use of air conditioner and lighting in the office . Localization systems based on sound such as footsteps or voice are superior to those based on image data in terms of privacy-consciousness and utility in the dark. As sound source localization methods, beamforming, subspace methods and TDOA-based methods are widely used. CSP (Cross-power Spectrum Phase) analysis is one of the most famous method to estimate TDOA. We have researched sound source localization methods using CSP analysis because it spends less calculation cost than beamforming or subspace methods. However, CSP analysis doesn't work well by itself in the real office environment because of sound lasting for a long time, reverberations, low SNR, and so on. The main cause of difficulty in sound source localization in such a case is the “weak correlation” between different sound signals. In this paper, it is shown that the bad effect of the “weak correlation” can be improved by our proposed methods, Wiener-filter-based CSP analysis and H-CSP (Histogram-CSP) analysis. H-CSP analysis is a statistics data analysis method for CSP coefficients which uses the histogram of CSP coefficients. In this research, the effectiveness of proposed methods are shown by sound source localization experiments in the case sound sources are stable and the case sound sources are moving.

Sound Source Localization through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network

Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

FBG sensor array-based-low speed impact localization system on composite plate

A fiber Bragg grating (FBG) sensors-based impact localization system on composite structure and a novel localization algorithm independent of wave velocity were proposed. Six FBG sensors constitute two isosceles right triangle FBG arrays. Impact signals were detected by a high-speed FBG interrogation system. Morlet wavelet transform was employed to extract time differences of impact signals. The straight lines equations, which are through impact source and FBG sensors of right-angled vertices of FBG arrays, can be obtained by the time differences. The coordinate of impact source is the intersection of straight lines. Testing experiments were carried out on composite plate within 400 mm × 400 mm monitor area. The experimental results showed that the maximum and average errors were 20.92 and 8.67 mm, respectively. This article provides a simple and stable impact source localization system independent of wave velocity.

On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone arrays

Sound source localization is an important topic in expert systems involving microphone arrays, such as automatic camera steering systems, human–machine interaction, video gaming or audio surveillance. The Steered Response Power with Phase Transform (SRP-PHAT) algorithm is a well-known approach for sound source localization due to its robust performance in noisy and reverberant environments. This algorithm analyzes the sound power captured by an acoustic beamformer on a defined spatial grid, estimating the source location as the point that maximizes the output power. Since localization accuracy can be improved by using high-resolution spatial grids and a high number of microphones, accurate acoustic localization systems require high computational power. Graphics Processing Units (GPUs) are highly parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. Emerging GPUs offer multiple parallelism levels; however, properly managing their computational resources becomes a very challenging task. In fact, management issues become even more difficult when multiple GPUs are involved, adding one more level of parallelism. In this paper, the performance of an acoustic source localization system using distributed microphones is analyzed over a massive multichannel processing framework in a multi-GPU system. The paper evaluates and points out the influence that the number of microphones and the available computational resources have in the overall system performance. Several acoustic environments are considered to show the impact that noise and reverberation have in the localization accuracy and how the use of massive microphone systems combined with parallelized GPU algorithms can help to mitigate substantially adverse acoustic effects. In this context, the proposed implementation is able to work in real time with high-resolution spatial grids and using up to 48 microphones. These results confirm the advantages of suitable GPU architectures in the development of real-time massive acoustic signal processing systems.

Transfer-Function-Based Calibration of Sparse EEG Systems for Brain Source Localization

This paper describes a transfer-function-based calibration technique for a sparse electroencephalography (EEG) source localization system. The forward solution and synthetic EEG signals are generated using the Brainstorm software. Various forms of calibration errors have been defined and induced on the nominal manifold, with different magnitudes. Two transfer-function-based calibration techniques are presented and the improvement brought about using these methods are compared against the results from benchmark scenarios. The performance of the proposed calibration techniques has been quantified in terms of the localization errors in the recovered sources before and after calibrating the system. Simulations suggest that in the presence of calibration errors, the proposed technique is capable of reducing the localization error as well as the number of falsely recovered sources.

Real-time Sound Source Localization on an Embedded GPU Using a Spherical Microphone Array

Abstract Spherical microphone arrays are becoming increasingly important in acoustic signal processing systems for their applications in sound field analysis, beamforming, spatial audio, etc. The positioning of target and interfering sound sources is a crucial step in many of the above applications. Therefore, 3D sound source localization is a highly relevant topic in the acoustic signal processing field. However, spherical microphone arrays are usually composed of many microphones and running signal processing localization methods in real time is an important issue. Some works have already shown the potential of Graphic Processing Units (GPUs) for developing high-end real-time signal processing systems. New embedded systems with integrated GPU accelerators providing low power consumption are becoming increasingly relevant. These novel systems play a very important role in the new era of smartphones and tablets, opening further possibilities to the design of high-performance compact processing systems. This paper presents a 3D source localization system using a spherical microphone array fully implemented on an embedded GPU. The real-time capabilities of these platforms are analyzed, providing also a performance analysis of the localization system under different acoustic conditions.

Acoustic Source Localization Based on Iterative Unscented Particle Filter

To solve the problem of tracking an acoustic source in noise and reverberation environment, a new method is proposed in pursuit of higher accuracy. First, this paper improves the unscented particle filter, which can add the latest measurement information to optimize the proposal distribution. Then, the likelihood function is constructed by calculating the microphone arrays’ output energy in the framework of the improved algorithm. Finally, the experiment results indicate that the proposed localization method can not only improves the accuracy of location estimation, but also can enhance the ability to resist noise and reverberation in the acoustic source localization system. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4816

Evaluation methods for sound source localization systems of speakers in a meeting room

The employing of sound source localization methods allows to evaluate location and head orientation of a speaker in a room. At present such systems are popular at development of intelligent support systems for smart meeting rooms. In this paper, a set of the metrics for performance evaluation of sound source localization systems as well as their integration with video monitoring systems are analyzed. Accuracy estimation of speaker positions, located on 32 chairs was carried out in the developed smart meeting room

Study on Odor Source Localization Method Based on Bionic Olfaction

This paper proposes an odor source localization method based on bionic olfaction. The special nasal cavity and turnable head make mammalian have excellent odor source localization ability. According to this principle, a turnable bionic odor sensing device is proposed by this paper for odor source localization system. This sensing device can rotate freely within the range level 360°, and the detection directions of its sensing channels are different. This also proposes a pattern recognition algorithm based on K-L transform to analyze the data collected by odor sensing device, and the features of odor source are extracted correctly. Experimental results for odor source localization demonstrate the feasibility of the proposed approach.