Generalized Cross Correlation Research Articles

An Analytical Method for 3-D Sound Source Localization Based on a Five-Element Microphone Array

An analytical method for 3D sound source localization based on a five-element microphone array is proposed in this work. With four time-difference-of-arrival (TDOA) values, the proposed method realizes the sound source localization by analytical solution. A new five-element microphone array configuration (which is characterized in that four microphones are in a same plane and at four vertices of a concave quadrilateral respectively, while the fifth microphone is out of the plane) is analyzed and studied. It is proved that with the proposed configuration, the five-element microphone array can obtain a unique and analytical solution. To investigate the effectiveness and the localization performance of the proposed method, a practical five-element microphone array is developed and 3D sound source localization experiments are carried out. The TDOA values are obtained by the generalized cross correlation (GCC) method based on the phase transform (PHAT). The experimental results show that the proposed method is an effective near-field localization method. The localization performance of the practical five-element microphone array is satisfactory. Compared with the conventional methods, the proposed method realizes the 3D localization by analytical solution and has the advantages of simple construction and lower computational complexity.

Cross-related microseismic location based on improved particle swarm optimization and the double-difference location method of jointed coal rock mass

The location algorithm is the core of microseismic monitoring, and the location results directly influence the effectiveness of the subsequent source analysis. We introduced the double-difference (DD) location method in seismic location into microseismic monitoring. We built a travel time equation of the layered velocity model using the ray-tracing theory and analyzed the waveform cross-correlation of the microseismic data using a generalized cross-correlation (GCC) technique. The improved particle swarm algorithm was successfully conducted to improve the randomness of the initial location. We then adopted the DD location method to precisely locate the secondary location, and it showed better location accuracy and computational efficiency than that by a traditional location algorithm based on the simplified geological model. The theoretical simulation and practice results in deep mining show that the proposed method significantly increased the location accuracy, and the results were concentrated and consistent with the field results. Moreover, we verified the importance of the velocity model for location accuracy. This algorithm also increased the ease of identifying fracture regions, providing the necessary theoretical and technical support for the source location of the jointed coal rock mass in deep mining.

High resolution time of arrival estimation algorithm for B5G indoor positioning

In beyond 5G (B5G) indoor positioning time of arrival (TOA) positioning method is an important one. Base stations measure reference signals sent by terminals to calculate the TOA. The accuracy of position calculation is highly dependent on the TOA estimation resolution. This paper proposes a high-resolution TOA estimation algorithm based on Sounding Reference Signal (SRS) of 5G communication system. The algorithm achieves high resolution estimation by two steps. The Least Mean Square (LMS) adaptive filtering method is used as the first step to obtain the integer part of TOA, which is a coarse estimation of TOA. In the second step, we use weighted Generalized Cross-Correlation (GCC) algorithm to obtain the fractional part of TOA in frequency domain to get a more precise TOA estimation. Moreover, the stability of the estimated results is improved by the threshold value setting. The proposed algorithm is simulated in both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. The simulation results show that the TOA error achieves 0.56 m in AWGN and 0.96 m in Rayleigh fading channels for 80% of test samples respectively.

A modified model predictive control method for frequency regulation of microgrids under status feedback attacks and time-delay attacks

As a promising technology to integrate renewable energy and enable decentralized energy management, microgrid (MG) offers an appealing network architecture due to its potential economic, environmental, and technical benefits. However, with the increasing deployment of intelligent devices and the growing network interconnectivity, communication channels and controllers for MGs become more vulnerable to emerging cyber threats. Two types of attacks are considered in this paper, including the data integrity attacks on the system status feedback and time-delay attacks which may disrupt the control of the MGs and lead to adverse consequences. A modified model predictive control (MPC) scheme is proposed for the secondary frequency control of MGs based on the online status switching method and generalized cross correlation (GCC) estimation to detect the real system status and time delay injected to the control system. The Euclidean metric is used in the online status switching method to obtain the real system states. Meanwhile, the GCC based delay estimation is developed to detect and track the time delay posed by attacks in the real-time operation. Case studies under different scenarios of the attacks are conducted, and the simulation results verify the effectiveness of the proposed MPC scheme under the cyberattacks.

Noise reduction in infrasound signals based on mask coefficient binary weighting – Generalized cross correlation – Non-negative matrix factorization algorithm

Infrasound noise reduction has two difficulties: the extremely limited datasets and the overlap of frequency bands. To overcome these problems, we present an algorithm, named MCWGCC-NMF, for noise reduction in multi-sensor recordings of mixture infrasound signals. The method is based on non-negative matrix factorization (NMF), and it is combined with spatial information estimated by generalized cross correlation (GCC) and mask coefficient binary weighting (MCW). The source dictionary masking by GCC-NMF, which uses spatial information of individual NMF atoms, is performed by frequency weighting and threshold selection to alleviate the frequency overlap. The results show that the proposed method achieves consistently better performance than the GCC-NMF on the infrasound dataset. Our study provides a signal processing method of infrasound noise reduction, which has the potential to further enhance the signals reception of infrasound arrays with wind noise reduction system (WNRS).

Audiovisual speaker indexing for Web-TV automations

The current paper introduces a multimodal framework to provide Web-TV automations for live broadcasting and overall big streaming data management. The term indexing refers to the spatiotemporal localization of speakers participating in a discussion panel. Multiple modalities acting in parallel form the data-driven decision-making pipeline. The automated workflow includes the tasks of active speaker detection and localization, frame selection, and creation of a semantically annotated database. For improved performance and robustness, an information fusion model is proposed, which makes use of different audio and visual modalities. Audio-driven Voice Activity Detection follows the Enhanced Temporal Integration methodology applied on a standard audio feature set. For the localization of the dominant audio source, the argument that maximizes the General Cross-Correlation method is calculated. The visual modalities include face and mouth detection and Visual Voice Activity Detection. A Long Short Term Memory network is trained with mouth image sequences to determine voice activity. The values of the audio and visual Voice Activity Detection modules, as well as the General Cross-Correlation result, are used to train an Adaptive Neuro-Fuzzy model, which is responsible for the final decision. Experimental results prove the superiority of the information fusion approach compared to unimodal audio or visual models.

Uncertainty Estimation for Ensemble Particle Image Velocimetry

We present a novel approach to estimate the uncertainty in ensemble particle image velocimetry (PIV) measurements. Ensemble PIV is widely used when the cross-correlation signal-to-noise ratio (SNR) is insufficient to perform a reliable instantaneous velocity measurement. Despite the utility of ensemble PIV, uncertainty quantification for this type of measurement has not been studied. The existing uncertainty quantification algorithms for PIV are developed and used only for instantaneous PIV measurement and do not account for the improved SNR in ensemble PIV. Existing instantaneous uncertainty quantification methods can be divided into direct and indirect categories. Indirect methods require calibration based on the effect of various image parameters (such as noise, particle size, density, velocity gradient, etc.) on the correlation SNR. Indirect methods have not been calibrated for error sources relevant in an Ensemble PIV measurement. Also, they have lower sensitivity to the error sources compared to direct approaches. Direct methods, such as the moment of correlation (MC) and Image Matching (IM), find the uncertainty based on the images and correlation planes without any calibration and are more reliable (Bhattacharya et al., 2018; Sciacchitano et al., 2013). Ensemble PIV is based on ensemble correlations; therefore, MC, which uses the generalized cross-correlation (GCC) plane as a measure of uncertainty, is the most suitable method to be modified to be applicable for the ensemble PIV. The GCC plane is the inverse Fourier transform of the phase correlation and represents the probability density function (PDF) of particles’ displacements (Bhattacharya et al., 2018; Eckstein and Vlachos, 2009). We replaced instantaneous GCC with ensemble GCC and modified MC’s normalization factor to account for the number of ensembles. The MC’s primary limitation is that it assumes a Gaussian shape for the PDF of displacements and estimate the standard deviation of the underlying PDF using a fitted Gaussian. However, the PDF deviates from Gaussian distribution due to velocity gradient or non-Gaussian random displacements. Therefore, MC’s reliability and applicability are reduced for flow fields with non-Gaussian PDFs. Also, our analysis shows that ensemble MC consistently underestimates the uncertainty. So, a generalized and reliable method for uncertainty quantification for ensemble PIV is needed.

A robust time delay estimation method for ultrasonic echo signals and elastography

Modern medicine cannot ignore the significance of elastography in diagnosis and treatment plans. Despite improvements in accuracy and spatial resolution of elastograms, robustness against noise remains a neglected attribute. A method that can perform in a satisfactory manner under noisy conditions may prove useful for various elastography methods. Here, we propose a method based on eigenvalue decomposition (EVD). In this method, the estimated time delay is defined as the index of the maximum element in the eigenvector that corresponds to the minimum eigenvalue in the covariance matrix of the received signal. Moreover, the implementation of the least-squares (LS) solution and the lower-upper (LU) decomposition contributes to improving the speed of computation and the accuracy of the estimation under low signal-to-noise ratio (SNR) conditions. To assess the performance of the proposed algorithm, it is evaluated along with generalized cross-correlation (GCC) and EVD. The simulation results clearly confirm that the jitter variance achieved in the proposed algorithm outperforms GCC and EVD in the proximity of the Cramer-Rau lower band. Moreover, our algorithm provides satisfactory performance in terms of variance and bias against sub-sample delay at low SNRS. According to the experimental results, the calculated values of the elastographic signal-to-noise ratio (SNRe) and the elastographic contrast-to-noise ratio (CNRe) of the proposed algorithm were 16.7 and 20.09, respectively, clearly better than the values of the other two methods. Furthermore, the proposed algorithm offers less execution time (about 30% of GCC), with a computational complexity equal to GCC and better than EVD.

Deep learning-based direction-of-arrival estimation for multiple speech sources using a small scale array.

A high resolution direction-of-arrival (DOA) approach is presented based on deep neural networks (DNNs) for multiple speech sources localization using a small scale array. First, three invariant features from the time-frequency spectrum of the input signal include generalized cross correlation (GCC) coefficients, GCC coefficients in the mel-scaled subband, and the combination of GCC coefficients and logarithmic mel spectrogram. Then the DNN labels are designed to fit the Gaussian distribution, which is similar to the spatial spectrum of the multiple signal classification. Finally, DOAs are predicted by performing peak detection on the DNN outputs, where the maximum values correspond to speech signals of interest. The DNN-based DOA estimation method outperforms the existing high resolution beamforming techniques in numerical simulations. The proposed framework implemented with a four-element microphone array can effectively localize multiple speech sources in an indoor environment.

Acoustic source localization with deep generalized cross correlations

One of the most popular techniques for Acoustic Source Localization is the Generalized Cross Correlation (GCC) and its use in Steered Response Power techniques (SRP). Nowadays, Deep Learning strategies may outperform these classical methods, but they are generally dependent on the room and sensor geometric configuration that are used during the training phases. Hence, they require adaptation and re-training when facing a new environment, which is a problem in practice as re-training requires labelling new data and running a complex training algorithm. In this work we use a Convolutional Deep Neural Network that transforms the GCC between two signals into a Gaussian shaped signal, that we call Deep Generalized Cross Correlation (DeepGCC). We combine DeepGCC estimations to create a 3D acoustic map, similarly to SRP techniques. This acoustic map can be further refined using a sparse generative model to recover the source position. Crucially, we can adapt the acoustic map to different microphone array geometries without retraining the DeepGCC network. We show that our method outperforms both classical approaches and recent Deep Learning strategies in real and simulated challenging scenarios with mismatched training-testing conditions, not requiring re-training with different sensor configurations or room environments.

An Unambiguous Synchronization Scheme for GNSS BOC Signals Based on Reconstructed Correlation Function.

Binary offset carrier (BOC) modulation is a new modulation method that has been gradually applied to the Global Satellite Navigation System (GNSS) in recent years. However, due to the multi-peaks in its auto-correlation function (ACF), it will incur a false lock and generate synchronization ambiguous potentially. In this paper, an unambiguous synchronization method based on a reconstructed correlation function is proposed to solve the ambiguity problem. First, through the shape code vector constructed in this paper, the general cross-correlation function (CCF) expression of the BOC modulated signal will be obtained. Based on the features of the signal correlation function, it is decomposed into a matrix form of trigonometric functions. Then, it generates two local signal waves using a specific method, then the proposed method is implemented to obtain a no-side-peak correlation function by reconstructing the cross-correlation between the received signal and the two local signals. Simulations showed that it fully eliminates the side-peak threat and significantly removes the ambiguity during the synchronization of the BOC signals. This paper also gives the improved structure of acquisition and tracking. The detailed theoretical deduction of detection probability and code tracking error is demonstrated, and the corresponding phase discrimination function is given. In terms of de-blurring ability and detection probability performance, the proposed method outperformed other conventional approaches. The tracking performance was superior to the comparison methods and the phase discrimination curve only had a zero-crossing, which successfully removed the false lock points. In addition, in multipath mitigation, it outperformed the ACF of the BOC signal, and performs as well as the autocorrelation side-peak cancellation technique (ASPeCT) for BOC(kn,n) signals.

Retrieving reflection arrivals from passive seismic data using Radon correlation

AbstractSince explosive and impulsive seismic sources such as dynamite, air guns, gas guns or even vibroseis can have a big impact on the environment, some companies have decided to record ambient seismic noise and use it to estimate the physical properties of the subsurface. Big challenges arise when the aim is extracting body waves from recorded passive signals, especially in the presence of strong surface waves. In passive seismic signals, such body waves are usually weak in comparison to surface waves that are much more prominent. To understand the characteristics of passive signals and the effect of natural source locations, three simple synthetic models were created. To extract body waves from simulated passive signals we propose and test a Radon-correlation method. This is a time-spatial correlation of amplitudes with a train of time-shifted Dirac delta functions through different hyperbolic paths. It is tested on a two-layer horizontal model, a three-layer model that includes a dipping layer (with and without lateral heterogeneity) and also on synthetic Marmousi model data sets. Synthetic tests show that the introduced method is able to reconstruct reflection events at the correct time-offset positions that are hidden in results obtained by the general cross-correlation method. Also, a depth migrated section shows a good match between imaged horizons and the true model. It is possible to generate off-end virtual gathers by applying the method to a linear array of receivers and to construct a velocity model by semblance velocity analysis of individually extracted gathers.

A Robust Steered Response Power Localization Method for Wireless Acoustic Sensor Networks in an Outdoor Environment.

The localization of outdoor acoustic sources has attracted attention in wireless sensor networks. In this paper, the steered response power (SRP) localization of band-pass signal associated with steering time delay uncertainty and coarser spatial grids is considered. We propose a modified SRP-based source localization method for enhancing the localization robustness in outdoor scenarios. In particular, we derive a sufficient condition dependent on the generalized cross-correlation (GCC) waveform function for robust on-grid source localization and show that the SRP function with GCCs satisfying this condition can suppress the disturbances induced by the grid distance and the uncertain steering time delays. Then a GCC refinement procedure for band-pass GCCs is designed, which uses complex wavelet functions in multiple sub-bands to filter the GCCs and averages the envelopes of the filtered GCCs as the equivalent GCC to match the sufficient condition. Simulation results and field experiments demonstrate the excellent performance of the proposed method against the existing SRP-based methods.

Acoustic imaging with conventional frequency domain beamforming and generalized cross correlation: a comparison study

Nowadays, acoustic cameras have become a standard tool for visualizing sound sources. The main post-processing techniques for generating acoustic images are associated with beamforming in the time or frequency domain. While many advanced techniques have been developed for the latter in order to achieve high resolution, such as deconvolution or inverse methods, the former have been less investigated. Recent studies have shown that time-domain beamforming can provide a narrow main lobe with low side lobe levels in different situations. However, a comparison of both techniques in terms of acoustic imaging does not exist. This paper provides a detailed comparison of conventional frequency-domain beamforming (CBF) and time-domain beamforming based on the generalized cross-correlation technique (GCC). First, numerical data are used for assessing both techniques. Then, experimental tests are carried out in a hemi-anechoic room using loudspeakers or power tools. As expected, acoustic images provided by CBF and GCC are found to be very similar but with different computation times. In order to improve the acoustic image, advanced techniques are considered (Clean-SC and the GCC based on geometric mean). When dealing with low frequency, Clean-SC is not able to detect both sources while the GCC based on geometric mean provides an accurate source separation.

Deep learning assisted sound source localization using two orthogonal first-order differential microphone arrays.

Sound source localization in noisy and reverberant rooms using microphone arrays remains a challenging task, especially for small-sized arrays. Recent years have seen promising advances on deep learning assisted approaches by reformulating the sound localization problem as a classification one. A key to the deep learning-based approaches lies in extracting sound location features effectively in noisy and reverberant conditions. The popularly adopted features are based on the well-established generalized cross correlation phase transform (GCC-PHAT), which is known to be helpful in combating room reverberation. However, the GCC-PHAT features may not be applicable to small-sized arrays. This paper proposes a deep learning assisted sound localization method using a small-sized microphone array constructed by two orthogonal first-order differential microphone arrays. An improved feature extraction scheme based on sound intensity estimation is also proposed by decoupling the correlation between sound pressure and particle velocity components in the whitening weighting construction to enhance the robustness of the time-frequency bin-wise sound intensity features. Simulation and real-world experimental results show that the proposed deep learning assisted approach can achieve higher spatial resolution and is superior to its state-of-the-art counterparts using the GCC-PHAT or sound intensity features for small-sized arrays in noisy and reverberant environments.

Analytical Model for the Relation Between Signal Bandwidth and Spatial Resolution in Steered-Response Power Phase Transform (SRP-PHAT) Maps

An analysis of the relationship between the bandwidth of acoustic signals and the required resolution of steered-response power phase transform (SRP-PHAT) maps used for sound source localization is presented. This relationship does not rely on the far-field assumption, nor does it depend on any specific array topology. The proposed analysis considers the computation of a SRP map as a process of sampling a set of generalized cross-correlation (GCC) functions, each one corresponding to a different microphone pair. From this approach, we derive a rule that relates GCC bandwidth with inter-microphone distance, resolution of the SRP map, and the potential position of the sound source relative to the array position. This rule is a sufficient condition for an aliasing-free calculation of the specified SRP-PHAT map. Simulation results show that limiting the bandwidth of the GCC according to such rule leads to significant reductions in sound source localization errors when sources are not in the immediate vicinity of the microphone array. These error reductions are more relevant for coarser resolutions of the SRP map, and they happen in both anechoic and reverberant environments.

An Echo Suppression Delay Estimator for Angle-of-Arrival Ultrasonic Indoor Localization

In this article, we propose a new robust, stable, accurate, and efficient time-delay estimation scheme for the angle-of-arrival (AoA) ultrasonic indoor single-source localization systems. The AoA technique employing microphone arrays and ultrasonic signal emitting sources has turned out to be a promising alternative to the conventional distance-based ultrawideband systems for indoor localization. The crux of AoA estimation lies in the derivation of time-delay estimates, which is typically handled by advanced digital signal processing approaches based on cross-correlation and matching algorithms. The calculation of the channel response for each received signal and the conversion from the time to the time-delay domain consent our echo suppression delay estimator (ESDE) to apply echo suppression, in order to overcome the typical challenges introduced by the channel in indoor environments. Moreover, the optimization of the cross-correlation algorithm through oversampling the cross power spectrum and the curve-fitting algorithm allows to ensure subsample precision results. The performances of our developed AoA estimation system are evaluated by carrying out real-world experiments and compared with the state-of-the-art generalized cross correlators. Our ESDE algorithm is shown to outperform several generalized solutions.

3-D Ultrasonic Localization of Transformer Patrol Robot Based on EMD and PHAT-β Algorithms

This article is concerned with a hyperboloid 3-D localization method of the transformer patrol robot based on the empirical mode decomposition (EMD) of ultrasonic signals and phase transform-beta (PHAT- $\beta $ ) generalized cross correlation (GCC). A linear sensor array is first employed to receive ultrasonic signals emitted by robot. The received signals are decomposed into several intrinsic mode functions (IMFs) of different scales by EMD. According to the correlation degree between IMFs and the received signals, the IMFs with large correlation coefficients are subsequently selected to reconstruct the signals. Furthermore, the PHAT- $\beta $ GCC algorithm is used to estimate the time delay between the different sensors. Finally, multiple hyperboloids formed by sensors are intersected to accurately localize the robot. The simulation and experimental results demonstrate that the maximum relative errors in three typical positions are 5.6%, 4.8%, and 5.2%. The maximum localization error is less than 15 mm in a laboratory scale model with a size of length 800 mm, width 450 mm, and height 500 mm.

Precise Indoor Positioning Based on Acoustic Ranging in Smartphone

Existing indoor positioning systems based on Wi-Fi or Bluetooth confront difficulties and problems, such as low positioning accuracy, unstable, and small-signal coverage. A system using an acoustic ranging signal is presented in this article, and it is available for almost all commercial off-the-shelf (COTS) smartphones. A solution was developed to detect the modulated signals transmitted by specially designed anchor stations at each consisting of a speaker. The detector is a two-stage algorithm based on a generalized cross correlation (GCC) method and a time-frequency-based feature extraction. The detected timestamps are converted to time-difference-of-arrivals (TDOAs) observables, which can be applied with a least-squares (LS) estimator to obtain the smartphone positions in static cases. Furthermore, an extended Kalman filter (EKF) is introduced to integrate data from inertial measurement units (IMUs) and acoustic TDOA ranging observables for estimating a better trajectory in dynamic cases. Experimental results show that the signal detection algorithm is accurate with a mean error of about 0.3-1 m in terms of ranging estimation. The proposed method offers a static positioning accuracy of better than 0.5 m. For dynamic tests, positioning accuracy of 0.5 m at 50% and 1.6 m at 95% is achieved with four anchors in the first scene. In the second scene with a large test site, it achieves a dynamic positioning accuracy of 0.5 m at 50% and 1.82 m at 95% with eight anchors, which is comparable with the positioning accuracy of the ultrawideband (UWB) technology.

Location of the Leakage From a Simulated Water-Cooling Wall Tube Based on Acoustic Method and an Artificial Neural Network

Water-cooling wall tube leakage accident is an important factor affecting the safe and economic operation of power station boilers. In this article, the acoustic method is applied to locate the leakage from a simulated water-cooling wall tube, and several key technologies are studied in order to identify the precise location of leakage source. Sound wave propagation path in ‘nonuniform temperature field is obtained to overcome the shortcoming that the traditional location technology always assumed that sound waves propagate along the straight line. Considering the refraction effect of sound waves, the reconstruction method based on radial basis function approximation with polynomial reproduction (RBF-PR) is used to reconstruct the temperature field. A generalized cross correlation with second correlation is applied to estimate the time delay of arrival (TDOA). According to the temperature field measured by acoustic method and the propagate time of sound waves, the location fingerprint method including the statistical correlation characteristics between the coordinate of fingerprint points and TDOA are constructed. Radial basis function artificial neural network (RBF ANN) is used to estimate the coordinate of leakage point. Numerical simulations and experimental study are implemented to evaluate the effectiveness of the proposed location system. An acoustic location test platform is constructed to study the location accuracy in different working conditions. The results indicate that considering the refraction effect, the reconstruction performance of temperature field is superior than that without the refraction effect, and acoustic method with RBF ANN algorithm can gain the leakage location with high accuracy and good antinoise ability in the above high-quality reconstructed nonuniform temperature distribution.