Source Localization System Research Articles

Fiber-optic bionic microphone based compact sound source localization system with extended directional range.

Traditional sound source localization (SSL) systems based on electret condenser microphone arrays are bulky because their localization accuracy depends on the size of the array. Inspired by the hearing mechanism of the parasitic fly Ormia ochracea, the localization accuracy of miniature bionic SSL devices breaks through the limitations of device size, but their ability to localize low-frequency sound sources over a wide angular range remains a challenge. In this work, a compact low-frequency SSL system with an extended directional range was prepared using two bionic micro-electro-mechanical system diaphragm based fiber-optic microphones, which form a non-coplanar array with a size of Φ44 mm × 13 mm. An algorithm for quantifying the azimuthal angle of a sound source is established for the prepared SSL system. Simulation and experimental results show that the prepared SSL system is capable of determining the propagation direction of acoustic signals with a frequency of less than 1 kHz in the azimuthal range from -90° to 90°, with a linear response in the range from -70° to 70°, and an angular measurement accuracy of the system within the range of ±7°.

Wind Source Localization System Based on a Palm-Sized Quadcopter

In this study, we implemented a compact wind direction sensor on a palm-sized quadcopter to achieve wind source localization (WSL). We designed an anemotaxis algorithm based on the sensor data and experimentally validated its efficacy. Anemotaxis refers to the strategy of moving upwind based on information on the wind direction, which is essential for tracing odors propagating through the air. Despite the limited research on quadcopter systems achieving WSL directly through environmental wind measurement sensors, debate remains regarding the relationship between sensor placement and the anemotaxis algorithm. Therefore, we experimentally investigated the placement of a wind direction sensor capable of estimating wind source direction even when propellers are rotating. Our findings demonstrated that placing the sensor 50 mm away from the enclosure of the quadcopter allowed accurate wind direction measurement without being affected by wake disturbances. Additionally, we constructed an anemotaxis algorithm based on wind direction and speed data, which we integrated into the quadcopter system. We confirmed the ability of the quadcopter to execute anemotaxis behavior and achieve WSL irrespective of environmental wind strength through wind source localization experiments.

Three-dimensional sound source localization system based on fiber optic sensor array with an adaptive algorithm

Sound Source Localization (SSL) based on microphone arrays nowadays is widely used in the fields of machinery rattle localization, weapon navigation, and robot hearing. However, there is also a need for SSL in some special environments, such as long-distance gas pipelines where there is a risk of leakage. In these environments, arrays built with ordinary microphones can be a safety hazard, which can be compensated by the External Fabry-Perot interferometer (EFPI) fiber-optic sensor's advantages of being uncharged, inexpensive, and highly sensitive. To expand the application areas of microphone array-based SSL techniques, this paper using four EFPI fiber-optic sensors to constructs a 1 m × 1 m × 1 m sensor array, which is used for real-time localization of sound sources based on three-wavelength adaptive intensity demodulation methods and Time Difference Of Arrival (TDOA) localization techniques. An improved method is proposed for the three-wavelength adaptive intensity demodulation method to avoid the occurrence of signal flips. Finally, formulas are provided for this sensor array to quickly calculate the sound source position based on the arrival delay. Several experiments show that the system can achieve sound source localization over a wide range with an average error of less than 3 cm.

Acoustic Source Localization System Based on Channel-Merged Fiber Optics Acoustic Sensing Array

Acoustic Source Localization System Based on Channel-Merged Fiber Optics Acoustic Sensing Array

A Survey of Sound Source Localization and Detection Methods and Their Applications.

This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.

An Intelligent Location Method for Power System Oscillation Sources Based on a Digital Twin

Aiming at the difficult problem of broadband oscillation localization in power systems, the intelligent localization method of an oscillation source based on a digital twin is proposed, and the oscillation source localization system is thus constructed. Firstly, a digital twin-based oscillation source localization method and its system architecture are proposed. Furthermore, an intelligent positioning method of the oscillation source, based on data-driven and mechanism fusion, is proposed. It includes three steps: oscillation signal preprocessing, oscillation modal analysis and oscillation source localization. For the oscillation signal preprocessing, the generative adversarial imputation network is used to repair the missing samples, and the super-resolution technique is used to realize the super-resolution measurement of broadband oscillation. In the oscillation modal analysis, the spectrum of the oscillation signal is extracted using the fast Fourier transform. To accurately locate the oscillation source, branch potential energy is used as the input to the data-driven model, such as LSTM and CNN. Finally, an oscillation source localization system is developed based on the digital twin workshop CloudPSS-XStudio, which can locate the oscillation source quickly and accurately.

A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival

In recent years, sound source localization, as a passive localization technique with higher safety and convenience compared with other localization techniques such as active emission of electromagnetic waves, has received more and more attention in academia. This paper researches and improves the far-field sound source localization algorithm based on the generalized cross-correlation method (GCC) Time Difference of Arrival (TDOA) estimation algorithm and completes the design and implementation of the microphone array sound source localization system. This paper adds a frequency divider to the traditional generalized correlation time delay estimation algorithm for pre-processing, sampling, and localization of sound source acoustic waves and adopts a low-cost microphone array deployment scheme as far as possible to improve the flexibility and practicality of the localization system; at the same time, the “Minimum Sphere Method” is used at the back end of the algorithm to classify the localization coordinates at different frequencies and, finally, output reasonable sound source coordinates. In the back-end of the algorithm, the “Minimum Sphere Method” is used to classify the localization coordinates at different frequencies and, finally, output the reasonable sound source coordinates. The experimental results show that the sound source localization system designed in this paper has good performance in terms of localization accuracy and cost-effectiveness and overcomes the failure of the generalized mutual correlation algorithm in the original application of high noise environment and multi-source environment localization.

Sound source localization model of cube microphone array

AbstractThis paper conducts in‐depth research on the sound source localization technology of the microphone array and designs a sound source localization system based on the cube microphone array. Firstly, the sound source localization model is established using the cube microphone array combined with the spherical near‐field acoustic holography. Secondly, the numerical and sound source localization simulations are carried out using the spherical wave. Finally, the simulation and experiment of sound source location for sound at 100, 1000, and 2000 Hz are carried out using the model. Both simulation and experimental results show that when the sound source frequency is 100 and 1000 Hz, the location of the sound source can be accurately located by using the sound source localization model of a cube microphone array, and the sound field reconstruction error is low. When the sound source frequency is 2000 Hz, the location of the sound source cannot be located, and the sound field reconstruction error is very high, which will cause the misjudgment of the sound source location.

CRB Weighted Source Localization Method Based on Deep Neural Networks in Multi-UAV Network

With the advent of the Internet of Things (IoT) era, the multi-unmanned aerial vehicle (UAV) networks have attracted great attention in the fields of source detection and localization. However, as a real-time signal processing performance of the UAV is limited by the computing speed and accuracy of the embedded hardware, the effectiveness of source localization is greatly reduced. Aiming at improving the accuracy and computational efficiency of source localization, a Cramer-Rao bound (CRB) weighted multi-UAV network source localization method is proposed based on the deep neural networks (DNN) and spatial spectrum fitting (SSF). The proposed source localization system is composed of UAVs equipped with a radar array. The source location can be achieved using the DOA of the sources signals of UAVs, but the accuracy and real-time performance of the conventional DOA estimation algorithms are not satisfactory, and the data fusion strategy of the conventional cross-location framework needs further improvement. In the proposed method, a DNN-based SSF denoted as the deep SSF (DeepSSF), is designed to achieve accurate DOA estimation. In the DeepSSF, the DOA estimation performance is guaranteed by the DNN’s strong nonlinear fitting ability and highly parallel structure. In addition, based on the obtained DOA information, the source is located once by every two UAVs. Finally, the source localization is realized based on the weighted CRB according to the principle that the more the DOA distribution deviates from zero, the lower the estimation accuracy. The simulation results verify the efficiency of the proposed method.

Stochastic Computing Design and Implementation of a Sound Source Localization System

Stochastic computing (SC) is an alternative computing paradigm that processes data in the form of uniform bit-streams. SC is fault-tolerant and can compute on small, efficient circuits. However, SC is primarily used in scientific research, and its practical implementations for end-users are rare. Digital sound source localization (SSL) is a useful signal processing technique that locates speakers using multiple microphones. SC has not been integrated into SSL in practice or theory. In this work, for the first time to the best of our knowledge, we implement an SSL algorithm in the stochastic domain and develop a functional SC-based sound source localizer. The practical part of this work shows that the proposed stochastic circuit does not depend on conventional analog-to-digital conversion and can process data in the form of pulse-width-modulated (PWM) signals. The proposed SC design consumes up to 39% less area than the conventional binary design. It can also consume less power depending on the computational accuracy, for example, 6% less power consumption for 3-bit inputs. We propose a new cross-correlation (CC) design based on the state-of-the-art Sobol bit-streams for further area and power saving. The proposed design utilizes a <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MUX</monospace> unit for bit-stream generation. It saves the area footprint up to 64% and the power consumption up to 82% compared to the counter-based SC design of CC, which relies on a comparator for bit-stream generation. The presented stochastic circuits, are not limited to SSL and are readily applicable to other practical applications such as radar ranging, wireless location, sonar direction finding, beamforming, and sensor calibration. The project’s source code is made available for public access.

Experimental study on 3D source localization in indoor environments with weak airflow based on two bionic swarm intelligence algorithms

Existing studies on using robots to locate indoor pollutant sources mainly focused on ventilated indoor environments, and rarely addressed unventilated or poorly ventilated indoor environments. This study aims to locate pollutant sources with unknown heights in real-world indoor environments with weak airflow. For this purpose, a 3D source localization system was first developed, which consists of three robots equipped with gas sensors that can be controlled to move up and down in the height direction. Next, two 3D source localization methods based on bionic swarm intelligence algorithms, namely the 3D whale optimization algorithm (WOA_3D) method and the 3D particle swarm optimization (PSO_3D) method, were validated in an unventilated indoor space with sources at different heights. The experimental results show that both the WOA_3D method and PSO_3D methods were highly adaptable to the change in source height and performed well in terms of success rate and localization efficiency. The comparison between the WOA_3D method and the PSO_3D method shows that the former method has better comprehensive performance. The WOA_3D method was also compared with its 2D simplified version, namely, the WOA_2D method, which further demonstrated the necessity of using the 3D source localization method when the source height is unknown.

Gaussian Process Regression for Single-Channel Sound Source Localization System Based on Homomorphic Deconvolution

To extract the phase information from multiple receivers, the conventional sound source localization system involves substantial complexity in software and hardware. Along with the algorithm complexity, the dedicated communication channel and individual analog-to-digital conversions prevent an increase in the system's capability due to feasibility. The previous study suggested and verified the single-channel sound source localization system, which aggregates the receivers on the single analog network for the single digital converter. This paper proposes the improved algorithm for the single-channel sound source localization system based on the Gaussian process regression with the novel feature extraction method. The proposed system consists of three computational stages: homomorphic deconvolution, feature extraction, and Gaussian process regression in cascade. The individual stages represent time delay extraction, data arrangement, and machine prediction, respectively. The optimal receiver configuration for the three-receiver structure is derived from the novel similarity matrix analysis based on the time delay pattern diversity. The simulations and experiments present precise predictions with proper model order and ensemble average length. The nonparametric method, with the rational quadratic kernel, shows consistent performance on trained angles. The Steiglitz-McBride model with the exponential kernel delivers the best predictions for trained and untrained angles with low bias and low variance in statistics.

A sound source localization method based on improved second correlation time delay estimation

The sound source localization (SSL) system based on the microphone array has important applications in audio and video conference, security monitoring and intelligent cockpit. However, the SSL method based on time difference of arrival is susceptible to ambient noise. Therefore, an improved second correlation delay estimation algorithm is proposed in this paper. The pure source signal is obtained by wavelet denoising, and then the time delay is calculated by the second correlation time delay estimation algorithm with the weighting functions of the smoothed coherence transform and the Roth processor. The position of the sound target is calculated from the time delay. Aiming at the moving target, an extended Kalman filter is introduced to track the moving trajectory of the sound source. The static and moving SSL simulations are conducted and the results of the proposed algorithm are compared with those of the single-weighted quadratic correlation (SQC) algorithm and the high-power quadratic correlation algorithm. The static sound source positioning errors of the proposed algorithm under −10 dB SNR are respectively 3.97 m and 5.86 m smaller than those of the HQC algorithm and the SQC algorithm. The moving SSL trajectory based on the proposed algorithm is still closest to the real track under −10 dB SNR. This indicates that the proposed algorithm has high precision and strong robustness for sound source location in the low signal-to-noise ratio (SNR) environment. In the experiment, the proposed algorithm can accurately calculate the direction of arrival (DOA) of static sound source and stably track DOA of moving sound source. This is consistent with the simulation results, which further verifies the effectiveness and practicability of the algorithm. This novel algorithm with high time delay estimation accuracy is of great significance for SSL in low SNR environment.

ORIGIN, an EU project targeting real-time 3D dose imaging and source localization in brachytherapy: Commissioning and first results of a 16-sensor prototype

The ORIGIN project targets the production and qualification of a real-time radiation dose imaging and source localization system for both Low Dose Rate (LDR) and High Dose Rate (HDR) brachytherapy treatments, namely radiotherapy based on the use of radioactive sources implanted in the patient’s body. This goal will be achieved through a 16-fiber sensor system, engineered to house in a clear-fiber tip a small volume of the scintillator to allow point-like measurements of the delivered dose. Each fiber is optically coupled to a sensor with single photon sensitivity (Silicon Photomultipliers — SiPMs) operating in counting mode. The readout is based on the CITIROC1A ASIC by WEEROC, embedded in the FERS-DT5202 scalable platform designed by CAEN S.p.A. Linearity and sensitivity together with the fiber response uniformity, system stability, and measurement reproducibility are key features for a instrument aiming to perform dose measurements. Characterization was carried out in the laboratory, using an X-ray cabinet; preliminary dose rate measurements were performed in clinical conditions.

Exploiting spatial diversity for increasing the robustness of sound source localization systems against reverberation

Acoustic reverberation is one of the most relevant factors that hampers the localization of a sound source inside a room. To date, several approaches have been proposed to deal with it, but have not always been evaluated under realistic conditions. This paper proposes exploiting spatial diversity as an alternative approach to achieve robustness against reverberation. The theoretical arguments supporting this approach are first presented and later confirmed by means of simulation results and real measurements. Simulations are run for reverberation times up to 2 s, thus providing results with a wider range of validity than in other previous research works. It is concluded that the use of systems consisting of several, sufficiently separated, small arrays leads to the best results in reverberant environments. Some recommendations are given regarding the choice of the array sizes, the separation among them, and the way to combine SRP-PHAT maps obtained from diverse arrays.

AI-based sound source localization system with higher accuracy

This paper focuses on the research of sound source detection technology. Combining the artificial intelligence (AI) and 6th generation (6G) communications, it is a hot topic to build a centralized microphone array to locate the sound source in a particular scene. However, degrees of vagueness still exist concerning the correlations and influences between the array establishment, localization implementation and its performance. To this end, we propose a sound source localization system (SSLS) with a high accuracy, ultra reliability and low latency communication. Meanwhile, an AI-based back propagation (BP) neural network enables the SSLS to predict the sound source position (SSP). SSLS is mainly composed of a data acquisition terminal and a data processing terminal. After relying on the Visual Studio and adopting the C/C#, a reliable SSP measurement is provided by the SSLS. When SSLS is running, sound source signals (SSSs) are collected through the array to obtain the time difference of arrival (TDOA) values in real time. Different from existing methods, this SSLS changes the SSS into a pulse signal by detecting the signal with a fixed energy intensity. At the same time, an intensity-based trigger interruption is designed to complete the TDOA acquisition. In this way, besides a reduction in TDOA calculation, the signal recognition is also improved. Further, while using TDOAs to locate the sound source, localization steps with a BP network are presented. Considering extreme sound source angles, a multi-plane based data fusion method is proposed. With the setting of different angles, especially extreme angles, we assess both quantitative and qualitative results of the proposed method, demonstrating that our method can achieve high-accuracy localization results regardless of angle degrees and the number of planes. Finally, comparison experiments confirm that the proposed SSLS significantly outperforms existing methods.

Providing a simple and efficient algorithm for passive sound localization

The sound source localization (SSL) system is one of the main approaches for detecting targets. This kind of localization method is carried out in a passive way, without the emission of any signals, and cannot be detected easily. This paper investigates the two-dimensional SSL of stationary targets using only three receivers. Given that, flow-induced noise of the bluff-bodies in viscous fluids is one of the most important noise generation sources, firstly, this noise is simulated numerically. Due to the distance between the receivers in an array, the sound reaches the receiver with time delay. High accuracy SSL needs highly accurate time delays measurement. In the following, time delays are obtained by calculating the cross-correlation in a time domain of sound signals arriving at the receiver array, and next, the bearing angle of the sound source is estimated. Later, using a new, simple (easy installation), fast (real-time), and accurate pure geometric algorithm, the source candidate locations are obtained. Then, using an elimination algorithm, the ambiguity of the noise source location is removed, and source imaginary locations are removed. Finally, this process is validated by a physical algorithm. Numerical results verify the effectiveness of this geometric algorithm.

A Review on Sound Source Localization Systems

A Review on Sound Source Localization Systems

Two-dimensional planar sound source localization based on microphone array

In recent years, people’s awareness of wildlife protection has been increasing, and the demand for wildlife observation and recording has increased. However, the existing sound source localization system needs to continuously collect sound signals and convert the time-domain signals of the sound into frequency-domain signals. These shortcomings will increase the power consumption of the device, adversely affect the battery life, and the sound source localization result is easily interfered by the echo of environmental noise and target sound waves. In this study, the algorithm and hardware are improved on the basis of the existing sound source positioning system, which can improve the positioning accuracy and reduce the power consumption and cost of the whole machine. The adaptive circuit is used to filter and adjust the sound signal in real time to eliminate the influence of the difference between the individual microphones in the microphone array, which makes the sound source localization system more robust to the environmental noise and the echo of the target sound wave, and more suitable for the forest scene of wildlife protection. Through the research of this sound source localization system, we hope to provide some new ideas for producing economical and practical sound source localization equipment and lay a foundation for the intelligent development of wildlife protection in the future.

A Defect Localization Approach Based on Improved Areal Coordinates and Machine Learning

The defects are usually generated during the structural materials subjected to external loads. Elucidating the position distribution of defects using acoustic emission (AE) technique provides the basis for investigating the failure mechanism and prevention of materials and estimating the location of the potentially dangerous sources. However, the location accuracy is heavily affected by both limitation of localization area and reliance on the premeasured wave velocity. Here, we propose a novel AE source localization approach based on generalized areal coordinates and a machine learning algorithmic model. A total of 14641 AE source location simulation cases are carried out to validate the proposed method. The simulation results indicate that even under various measurement error conditions the AE sources could be effectively located. Moreover, the feasibility of the proposed approach is experimentally verified on the AE source localization system. The experiment results show that the mean localization error of 3.64 mm and the standard deviation of 2.61 mm are obtained, which are 67.55% and 75.46% higher than those of the traditional method.