Sound Source Localization Method Research Articles

A High-Resolution Time Reversal Method for Target Localization in Reverberant Environments

Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust.

Sound Source Localization Method Based on Time Reversal Operator Decomposition in Reverberant Environments

Predicting sound sources in reverberant environments is a challenging task because reverberation causes reflection and scattering of sound waves, making it difficult to accurately determine the position of the sound source. Due to the characteristics of overcoming multipath effects and adaptive focusing of the time reversal technology, this paper focuses on the application of the time reversal operator decomposition method for sound source localization in reverberant environments and proposes the image-source time reversal multiple signals classification (ISTR-MUSIC) method. Firstly, the time reversal operator is derived, followed by the proposal of a subspace method to achieve sound source localization. Meanwhile, the use of the image-source method is proposed to calculate and construct the transfer matrix. To validate the effectiveness of the proposed method, simulations and real-data experiments were performed. In the simulation experiments, the performance of the proposed method under different array element numbers, signal-to-noise ratios, reverberation times, frequencies, and numbers of sound sources were studied and analyzed. A comparison was also made with the traditional time reversal method and the MUSIC algorithm. The experiment was conducted in a reverberation chamber. Simulation and experimental results show that the proposed method has good localization performance and robustness in reverberant environments.

Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition.

To address the challenge of accurately locating unmanned aerial vehicles (UAVs) in situations where radar tracking is not feasible and visual observation is difficult, this paper proposes an innovative acoustic source localization method based on improved Empirical Mode Decomposition (EMD) within an adaptive frequency window. In this study, the collected flight signals of UAVs undergo smoothing filtering. Additionally, Robust Empirical Mode Decomposition (REMD) is applied to decompose the signals into Intrinsic Mode Function (IMF) components for spectrum analysis. We introduce a sliding frequency window with adjustable bandwidth, which is automatically determined using a Grey Wolf Optimizer (GWO) with a sliding index. This window is used to lock and extract specific frequencies from the IMFs. Based on predefined criteria, the extracted IMF components are reconstructed, and trigger signal times are analyzed and recorded from these reconstructed IMFs. The time differences between sensor receptions are then calculated. Furthermore, this study introduces the Chan-Taylor localization algorithm based on weighted least squares. This advanced algorithm takes sensor time delay parameters as input and solves a set of nonlinear equations to determine the target's location. Simulations and real-world signal tests are used to validate the robustness and performance of the proposed method. The results indicate that the localization error remains below 5% within a 15 m × 15 m measurement area. This provides an efficient and real-time method for detecting the location of small UAVs.

Proposal of Practical Sound Source Localization Method Using Histogram and Frequency Information of Spatial Spectrum for Drone Audition

Proposal of Practical Sound Source Localization Method Using Histogram and Frequency Information of Spatial Spectrum for Drone Audition

Research on Sound Source Localization of Multiple Fixed Targets Based on Machine Learning and Distributed Arrays

This paper studies a sound source localization method of multiple fixed targets based on machine learning and distributed arrays. In an outdoor open field, a three-line array was applied to collect array data and calculate latency characteristics. Then multiple classification models were established and trained. Finally, the locations of the sound source points were predicted by those models, in which the support vector machine (SVM), the nearest node (KNN), and the naive Bayesian model achieved 100% localization accuracy. Compared to the conventional method, this method has three significant advantages: First, it does not rely on the microphone channel order and does not need to be calibrated in advance, which simplifies the localization process; Second, it can fulfill high accuracy requirements, especially suitable for the scene of multiple fixed targets; Third, it has the advantage of incremental learning, as the times of localization rises, the training set is continuously enriched and the localization results become more precise.

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.

A Basic Study on a Method for Sound Source Localization Using Distributed Acoustic Measurement Equipment

The purpose of this study is to develop a system that can detect sound source localization in a plane using a cross-correlation function between acoustic signals recorded by microphones distributed in a plane. An anechoic chamber experiment showed that the system was capable of estimating the location of stationary sound sources with plus or minus 20 mm accuracy. As well as estimating the locations of moving sources of sound, the system was also able to locate them with an error of approximately 10 mm. A Raspberry Pi equipped with an A/D converter, microphone, and GPS module was then used to develop an acoustic measurement device. This was used to identify sound source location by using time synchronization with GPS signals in an outdoor space.

Sound source localization method based time-domain signal feature using deep learning

Deep learning, as the most commonly used machine learning algorithm, is widely used in various fields. In the field of acoustics, deep learning methods are combined with frequency-domain features of signals to locate sound sources. The commonly frequency domain features include microphones array Cross-spectral-Matrix(CSM) and Short Time Fourier Transform(STFT). However, the use of frequency-domain features often leads to the loss of partial signal information and increases the computational complexity. This paper proposed a novel sound source localization algorithm based on time-domain features, which uses convolutional neural network(CNN) as a medium to achieve mapping from time-domain features to sound source locations. This method does not rely on any basic signal processing algorithm, and directly uses time-domain sampling points as network inputs for sound source localization. The application simulation shows that the proposed method can achieve precise localization and low side-lobe effect under different testing conditions. Once the network training is completed, the testing accuracy under different conditions is above 95%, with a maximum of 100%.

Direction-of-arrival estimation for acoustic signals based on direction-dependent parameter tuning of a bioinspired binaural coupling system

Bioinspired methods for sound source localization offer opportunities for resource reduction as well as concurrent performance improvement in contrast to conventional techniques. Usually, sound source localization requires a large number of microphones arranged in irregular geometries, and thus has high resource requirements in terms of space and data processing. Motivated by biology and using digital signal processing methods, an approach that adapts the coupled hearing system of the fly Ormia ochracea with a minimally distant two-microphone array is presented. Despite its physiology, the fly is able to overcome physical limitations in localizing low-frequency sound sources. By exploiting the filtering effect of the coupling system, the direction-of-arrival of the sound is determined with two microphones at an intermediate distance of 0.06 m. For conventional beamforming algorithms, these physical limitations would result in degraded localization performance. In this work, the bioinspired coupling system is analyzed and subsequently parameterized direction-sensitive for different directions of incidence of the sound. For the parameterization, an optimization method is presented which can be adopted for excitation with plane as well as spherical sound wave propagation. Finally, the method was assessed using simulated and measured data. For 90% of the simulated scenarios, the correct direction of incidence could be determined with an accuracy of less than 1∘ despite the use of a minimal distant two-microphone array. The experiments with measured data also resulted in a correct determination of the direction of incidence, which qualifies the bioinspired method for practical use in digital hardware systems.

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.

Research on structural sound source localization method by neural network

To solve problems related to much calculation to adapt to complex scenes in traditional structural sound source localization, this paper proposes a method based on neural network. The structural sound source at other positions was stimulated by successively striking 36 grid centers on the surface of the plate. The time delay between different accelerometer signals was considered as the input, and the location of the predicted sound source was considered as the output. The influence of the number of test sets and epoch training times on sound source localization accuracy was discussed. These results show that with the increase in the epoch training times, the number of test set decreases, and the number of training set increases, increasing the sound source localization accuracy of backpropagation neural network. However, these error conditions will frequently appear due to the overfitting phenomenon. When the epoch is trained to 50,000 times, and the quantity of the test set is 4, the backpropagation neural network has the best localization accuracy with an order of magnitude of 10−3 in error, and the localization error scope of the plate is between 0.01 and 0.1 m.

Study on sound source localization inside a structure using a domain transfer model for real-world adaption of a trained model

Sound source localization (SSL) is important to reduce noise of products such as machinery and electrical appliances. Currently, SSL methods have been proposed that use the correlation of time-frequency signals of sound waves observed by multiple microphones. However, the application of these SSL methods are limited to the case where the acoustic signals can be directly observed. In the case of estimating the location of a sound source inside a structure from outside the structure, these methods are not applicable because the acoustic signal is observed as indirect sound. An SSL method using deep neural network (DNN) and computer-aided engineering (CAE) was proposed to estimate sound sources inside structures. This method successfully estimates the location of sound sources inside a structure from signals observed by accelerometers installed on the outer surface of the structure in both CAE and real domains. Nevertheless, the proposed method still has the challenge of adapting DNNs trained in CAE domain to real domain. In this research, deep convolution auto-encoder (DCAE) was used as a domain transformation model built from CAE and actual experimental data. DCAE contributed to the improvement of the SSL performance of the trained DNN over the baseline.

Equivalent source method based Near-field acoustic holography using machine learning

The equivalent source method has been one of the most commonly used methods for sound source localization. It involves equivalent sources spread over the source plane (or region). The pressure fields from these equivalent sources are usually spherical harmonics. But, the spherical harmonic fields are derived for the Sommerfeld boundary condition with no reflection or reverberation. Data-driven methods help perform sound source localization in a reverberant environment when no prior information about the surroundings is available. The methods studied are linear regression (LR) with Adam, linear regression with L-BFGS, multi-layer perceptron (MLP) with one and two hidden layers. The simulations are conducted for two monopoles in rooms with different reverberation times and compared with one norm convex optimization (L1CVX). It is observed that overall, LR with L-BFGS gave the best results. Also, for low reverberation time, LR with L-BFGS was able to localize the sources better than L1CVX.

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.

A fast deconvolution method for multiple sound source localization based on Hilbert curve

To improve the resolution and efficiency of the multiple sound source localization, a fast deconvolution method based on the Hilbert curve, called CLEAN-VH, is developed in this paper. Firstly, the initial cross-spectral matrix of the measured array signal is decomposed by eigenvalue decomposition, and a new cross-spectral matrix function is constructed using the eigenvalues and eigenvectors. Then, the function is used to update the cross-spectral matrix in the process of deconvolution iteration (CLEAN), and the Hilbert curve is introduced to improve the computational efficiency of the output power in the monitoring area. Finally, the distribution of multiple sound sources can be obtained by superimposing the clean beams and residual power spectrum. The superiority of CLEAN-VH is proved by analyzing the key parameters of the method. The search area optimization based on Hilbert curve greatly alleviates the grid mismatch problem and improves the performance of the proposed method. Through the simulation and experimental comparisons with the other three algorithms, such as CLEAN, FB and CLEAN-SC, the proposed CLEAN-VH achieves high-resolution localization of multiple sound sources, better dynamic range and higher computation efficiency. The proposed method provides another choice for the localization of multiple sound sources.

A vehicle whistle database for evaluation of outdoor acoustic source localization and tracking using an intermediate-sized microphone array

Acoustic source localization and tracking (ASLT) are always hot topics for their wide applications, such as binaural hearing aids, smart devices, and audio–video conference systems. Numerous ASLT algorithms have already been proposed, however only very limited open databases were available for evaluation their performance. To date, most of these existing databases were recorded indoors using only a limited number of microphones. To the best of our knowledge, it still lacks of available open databases to evaluate ASLT algorithms for outdoor applications using an intermediate-sized microphone array. This paper describes a new database for evaluation ASLT algorithms, where a planar array with 32 MEMS microphones were designed to record a great number of vehicle whistles in outdoor environments with the vertical distance about 8 m away from the center of this array to the road surface. Besides the detailed description of this database, an example using the steered response power-phase transform sound source localization method was provided to show that the vehicle whistle database has been successfully built. The vehicle whistle database and the demo code have been uploaded and available online.

Sound source localization of harmonic sources in entire 3D space using just 5 acoustic signals

Sound Source Localization (SSL) has numerous applications namely machinery noise control, machinery fault localization, localization of explosives and weaponry in defense, development of hearing aids and robot’s auditory system. Beamforming and holographic SSL methods require numerous microphones and measurements for predicting source location; hence are highly expensive and time consuming. The simpler alternatives require less microphones; but are inaccurate, require high SNR, and have space of non-convergence where source cannot be uniquely localized. There is also a lack of method to localize pure harmonic sources. This paper proposes a method that addresses these limitations by accurately and quickly identifying the exact location of a harmonic sound source in the complete 3D space in acoustic free-field using just five acoustic signals. The novelties of this method are: new array design, spatially averaged Time Delay Estimation (TDE), TDE-ER (Energy Ratio) based localization, ER to remove mirror points, spectral de-noising, and solution of non-convergence. The method is rigorously tested through numerous simulations and real-life free-field experiments. A performance comparison shows that proposed method is more accurate (6.7% error) compared with the existing 3D SSL technique (11.7% error). Overall, it accurately identifies the coordinates of dominant sound source with 9.17% mean error, in presence of inherent sensor noise (14 dB SNR onwards) and background noise (8 dB SNR onwards) in complete 3D space. Interestingly, the sound source location, and sound source frequency is found to effect the consistency of TDE-based SSL methods. The presented work paves way for further advancement in 3D SSL.

Scalable real-time sound source localization method based on TDOA

Scalable real-time sound source localization method based on TDOA

A survey of sound source localization with deep learning methods.

This article is a survey of deep learning methods for single and multiple sound source localization, with a focus on sound source localization in indoor environments, where reverberation and diffuse noise are present. We provide an extensive topography of the neural network-based sound source localization literature in this context, organized according to the neural network architecture, the type of input features, the output strategy (classification or regression), the types of data used for model training and evaluation, and the model training strategy. Tables summarizing the literature survey are provided at the end of the paper, allowing a quick search of methods with a given set of target characteristics.

A fast 3D-MUSIC method for near-field sound source localization based on the bat algorithm

To improve the computation and real-time performances of the multiple signal classification (MUSIC) algorithm in 3D space, a fast sound source localization method based on the bat algorithm (BA) and the 3D-MUSIC, called BA-based 3D-MUSIC algorithm (3D-BMUSIC), is presented in this paper. 3D-BMUSIC greatly reduces the computation load by replacing the regular grid search with the BA. First, the near-field spherical wave model is established to obtain the spectral function of the 3D-MUSIC. Then, the spectral function is defined as the fitness function, which calculates the fitness value corresponding to each bat position. Finally, the global optimal bat position with the largest fitness value, as sound source localization, is obtained by successive iteration and sorting. The simulation and experiment show that 3D-BMUSIC accurately estimates the DOA and distance of near-field sources, and the root-mean-square error (RMSE) of 3D-BMUSIC is less than that of 3D-MUSIC. In addition, 3D-BMUSIC effectively reduces the computation time by approximately 96–98%. With shorter computation time and higher efficiency, 3D-BMUSIC promotes hardware implementation and is more suitable for high-precision localization of near-field sound sources.