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Sound source localization remains a critical task for increasing sufficiently the location accuracy and typically decreasing the computational complexity of a real-time passive or active target tracking by multiple-microphone in topology-based concurrent sensor arrays for far- or near-filed domains. This paper properly presents a passive real-time localization method scientifically based on the time difference of arrival signal in reliably estimating the arrival angles for meaningfully improving the location accuracy and typically decreasing the prediction time of a stationary source in a three-dimensional (3D) model for an outdoor environment in free-field conditions. The proposed scalable distributed method significantly increases the localization accuracy by integrating local direction-finding information of two concurrent parallel microphone arrays and naturally deriving a specific piece of global location-finding information to sufficiently reduce the predictions' run-time. The designing approach accurately represents a two-step process to properly implement a suitable decomposition method and gently apply an interpolation technique in the near-filed ideal domain. The primary goal of our innovative design realistically is to seemingly indicate the key role of hardware architecture and popularly use suitable techniques in complexity reduction and accuracy improvement of the intelligent model for future scalable localization systems to progressively increase outstanding performance. The empirical experiments using different real and simulated datasets show the modern TDOA-SSL method ordinarily has a low-time 360 msec in 3D accurately estimating the prime location with an average error of 12.07 cm in the near-field and 296 cm in the far-field. In key addition, the proposed method invariably is robust to ambient noise and sound reflection in the near- and far-field. However, the average accuracy of the proposed system efficiently is 99.43% with an error factor of 0.19% for the near-field and 99.70% with an error factor of 0.016% for the far-field for the used range of 100 m and 1 km, respectively. Performance evaluation of the proposed method sufficiently shows the real-time prediction and the reasonable accuracy of the specific prediction positively enhances by carefully applying the geometric architectural specification. [ABSTRACT FROM AUTHOR]

Animals that migrate in mixed-species groups may communicate with both conspecific and heterospecific individuals, providing a low-cost mechanism for navigation whenever individuals share similar migratory routes or destinations. Many migratory birds produce calls while flying, but the function of these calls, and the forces contributing to their evolution, are poorly known. We studied flight calls in mixed-species groups of wood warblers (Parulidae), a biodiverse group of migratory songbirds. We used a spatial approach to examine whether acoustic similarity of flight calls varies with group composition, recording flight calls of mixed-species flocks with a wireless microphone array and triangulating the positions of birds in three dimensions. We found that the acoustic similarity of flight calls was correlated with spatial proximity: birds with similar calls fly closer together during migration. We also found relationships between acoustic similarity, flock size and mixed-species flock diversity: birds with similar calls fly in smaller flocks and in flocks with lower species diversity. Our results support the idea that migrating birds use flight calls to maintain contact with acoustically similar individuals in mixed-species flocks, with communication transcending species boundaries. These results suggest that acoustically similar flight calls are used as cues of group assembly for migratory animals. This article is part of the theme issue 'Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes'. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) compressive beamforming (CB) with planar microphone array for acoustic source identification has attracted much attention due to its advantages of wide identification space, applicability to both coherent and incoherent acoustic sources, and clear imaging. It realizes acoustic source identification by establishing and solving the underdetermined equations between the sound pressure measured by the microphone array and the source strengths of the discretized grid points in the target area. Convex relaxation algorithms and greedy algorithms are often used to solve these underdetermined equations. CB based on convex relaxation requires the sensing matrix to satisfy the restricted isometry property and needs large calculating consumption, which is unsuitable for large-scale problems. CB based on greedy algorithms may achieve a locally optimal solution instead of the global optimum, and it is easily affected by the coherence of the sensing matrix columns, so its performance is sensitive to the grid spacing. To take into account both the robustness of acoustic source identification performance and high computational efficiency, we adapted sparse Bayesian learning (SBL) to solve the above underdetermined equations and proposed a 2D-SBL-CB in this paper. It first assumes the signal prior and noise prior to derive the posterior probability distribution of the source strength, and then adaptively estimates the hyperparameters of the sparse prior of source strength based on a type-II maximum likelihood, i.e., by maximizing the evidence, to achieve source identification. Both simulations and experiment show that the proposed 2D-SBL-CB is robust to grid spacing and has stable source identification performance. It also enjoys high computational efficiency, strong spatial resolution ability, and strong anti-noise interference, with optimal overall performance. [ABSTRACT FROM AUTHOR]

Due to the growing number of drones and unmanned aerial vehicles, the aeroacoustic noise radiated by airfoils in moderate Reynolds-number flows increasingly becomes the focus of interest. In the present paper, the flow-generated noise of two basic (a flat plate and a cambered plate) and two conventional (RAF6 and NACA 0012) airfoil profiles is investigated in an open-jet wind tunnel in the interval of Reynolds numbers between 60,000 and 140,000. The measurements were carried out with a phased array microphone. The measurement data were evaluated using frequency–domain beamforming and the analysis of variance statistical method. The results show that the emitted noise is nearly independent of the angle of attack but dependent on the airfoil geometry and the chordwise location. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects. [ABSTRACT FROM AUTHOR]

Speech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered response power-phase transform (SRP-PHAT) weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position information. Since theGRUnetwork has the advantage of processing time series data with faster training speed and fewer training parameters, the GRUmodel is adopted to process the separation features of several sequential frames in the same sub-band to estimate the ideal RatioMasking (IRM). The proposed algorithm decomposes themixture signals into time-frequency (TF) units using gammatone filter bank in the frequency domain, and the target speech is reconstructed in the frequency domain bymasking themixture signal according to the estimated IRM. The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost. Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech separation in noisy and reverberant environments, provides good performance in terms of speech quality and intelligibility, and has the generalization capacity to reverberate. [ABSTRACT FROM AUTHOR]

More and more smart home devices with microphones come into our life in these years; it is highly desirable to connect these microphones as wireless acoustic sensor networks (WASNs) so that these devices can be better controlled in an enclosure. For indoor applications, both environmental noise and room reverberation may severely degrade speech quality, and thus both of them need to be removed to improve users' experience. For this goal, this paper proposes a parallel processing framework of distributed beamforming and multichannel linear prediction (DB-BFMCLP), which consists of generalized sidelobe canceler and multichannel linear prediction for simultaneous speech dereverberation and noise reduction in WASNs. By sharing a common desired response vector, the proposed DB-BFMCLP can provide a significant reduction in communication bandwidth without sacrificing performance. The convergence guarantee of the DB-BFMCLP to its centralized implementation is derived mathematically. Simulation results verify the superiority of the proposed method to the existing related methods in noisy and reverberant scenarios. [ABSTRACT FROM AUTHOR]

High-resolution compressive imaging via a flexible multimode fiber is demonstrated using a swept-laser source and wavelength dependent speckle illumination. An in-house built swept-source allowing for independent control of bandwidth and scanning range is used to explore and demonstrate a mechanically scan-free approach for high-resolution imaging through an ultrathin and flexible fiber probe. The computational image reconstruction is shown by utilizing a narrow sweeping bandwidth of < 10 nm while acquisition time is decreased by 95% compared to conventional raster scanning endoscopy. Demonstrated narrow-band illumination in the visible spectrum is vital for the detection of fluorescence biomarkers in neuroimaging applications. The proposed approach yields device simplicity and flexibility for minimally invasive endoscopy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The presented work aims to adapt a Geiger‐based method and localize pendulum impacts applied on a rigid steel plate using the data set of a 112 microphone array. The advantage compared to measurements with piezoelectric sensors, where normally a number of sensors are involved, is the superior number of microphones. To solve the Geiger algorithm on the three‐dimensional domain, at least data from three sensors and the primary wave velocity are required (four equations). Moreover, if the system of equations is overdetermined, an error minimization can be performed, for example, with the least‐squares method. Since in this work the distance between array and plate is constant, the algorithm can be even reduced to a two‐dimensional problem (only three equations required). Indeed, the superior overdetermination of 112 microphone data allows to index the optimal data set by combing microphone data and thus achieve the best localization results for the optimal microphone. This study is relevant to the scientific community because it will allow uncontrolled events cracks or other damage to be localized with a great accuracy and reliability. [ABSTRACT FROM AUTHOR]

Most existing direction-of-arrival (DOA) estimation algorithms are intended for single-frequency use. However, the majority of real sound fields are wideband, and the application of these techniques then becomes computationally expensive. In this paper, a fast DOA estimation method for use with wideband sound fields is constructed from only a single observation of the array signal based on the properties of a space of spherically band-limited functions. The proposed method can be applied to any element arrangement and spatial dimensions, and the computational load is only dependent on the number of microphones in the array. However, because this method does not use time information, forward-backward identification of the arriving waves is not possible. Therefore, the proposed DOA estimation method is limited to a half-space. Numerical simulations of multiple sound waves arriving from a half-space show that the proposed method offers good processing performance when applied to pulse-like broadband sound fields. The results also indicate that the method is capable of tracking DOAs in real time, even when these DOAs vary rapidly. [ABSTRACT FROM AUTHOR]

Active noise control (ANC) systems are commonly designed to achieve maximal sound reduction regardless of the incident direction of the sound. When desired sound is present, the state-of-the-art methods add a separate system to reconstruct it. This can result in distortion and latency. In this work, we propose a multi-channel ANC system that only reduces sound from undesired directions, and the system truly preserves the desired sound instead of reproducing it. The proposed algorithm imposes a spatial constraint on the hybrid ANC cost function to achieve spatial selectivity. Based on a six-channel microphone array on a pair of augmented eyeglasses, results show that the system minimized only noise coming from undesired directions. The control performance could be maintained even when the array was heavily perturbed. The proposed algorithm was also compared with the existing methods in the literature. Not only did the proposed system provide better noise reduction, but it also required much less effort. The binaural localization cues did not need to be reconstructed since the system preserved the physical sound wave from the desired source. [ABSTRACT FROM AUTHOR]

Speech processing algorithms, especially sound source localization (SSL), speech enhancement, and speaker tracking are considered to be the main fields in this application. Most speech processing algorithms require knowing the number of speakers for real implementation. In this article, a novel method for estimating the number of speakers is proposed based on the hive shaped nested microphone array (HNMA) by wavelet packet transform (WPT) and 2D sub-band adaptive steered response power (SB-2DASRP) with phase transform (PHAT) and maximum likelihood (ML) filters, and, finally, the agglomerative classification and elbow criteria for obtaining the number of speakers in near-field scenarios. The proposed HNMA is presented for aliasing and imaging elimination and preparing the proper signals for the speaker counting method. In the following, the Blackman–Tukey spectral estimation method is selected for detecting the proper frequency components of the recorded signal. The WPT is considered for smart sub-band processing by focusing on the frequency bins of the speech signal. In addition, the SRP method is implemented in 2D format and adaptively by ML and PHAT filters on the sub-band signals. The SB-2DASRP peak positions are extracted on various time frames based on the standard deviation (SD) criteria, and the final number of speakers is estimated by unsupervised agglomerative clustering and elbow criteria. The proposed HNMA-SB-2DASRP method is compared with the frequency-domain magnitude squared coherence (FD-MSC), i-vector probabilistic linear discriminant analysis (i-vector PLDA), ambisonics features of the correlational recurrent neural network (AF-CRNN), and speaker counting by density-based classification and clustering decision (SC-DCCD) algorithms on noisy and reverberant environments, which represents the superiority of the proposed method for real implementation. [ABSTRACT FROM AUTHOR]

Background: Habitat structure strongly influences niche differentiation, facilitates predator avoidance, and drives species-specific foraging strategies of bats. Vegetation structure is also a strong driver of echolocation call characteristics. The fine-scale assessment of how bats utilise such structures in their natural habitat is instrumental in understanding how habitat composition shapes flight- and acoustic behaviour. However, it is notoriously difficult to study their species-habitat relationship in situ. Methods: Here, we describe a methodology combining Light Detection and Ranging (LiDAR) to characterise three-dimensional vegetation structure and acoustic tracking to map bat behaviour. This makes it possible to study fine-scale use of habitat by bats, which is essential to understand spatial niche segregation in bats. Bats were acoustically tracked with microphone arrays and bat calls were classified to bat guild using automated identification. We did this in multiple LiDAR scanned vegetation plots in forest edge habitat. The datasets were spatially aligned to calculate the distance between bats' positions and vegetation structures. Results: Our results are a proof of concept of combining LiDAR with acoustic tracking. Although it entails challenges with combining mass-volumes of fine-scale bat movements and vegetation information, we show the feasibility and potential of combining those two methods through two case studies. The first one shows stereotyped flight patterns of pipistrelles around tree trunks, while the second one presents the distance that bats keep to the vegetation in the presence of artificial light. Conclusion: By combining bat guild specific spatial behaviour with precise information on vegetation structure, the bat guild specific response to habitat characteristics can be studied in great detail. This opens up the possibility to address yet unanswered questions on bat behaviour, such as niche segregation or response to abiotic factors in interaction with natural vegetation. This combination of techniques can also pave the way for other applications linking movement patterns of other vocalizing animals and 3D space reconstruction. [ABSTRACT FROM AUTHOR]

In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1 s and 0.5 s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. The database is publicly available (https://zenodo.org/record/7389996). [ABSTRACT FROM AUTHOR]

This paper presents a sound source localization (SSL) model based on residual network and channel attention mechanism. The method takes the combination of log-Mel spectrogram and generalized cross-correlation phase transform (GCC-PHAT) as the input features, and extracts the time–frequency information by using the residual structure and channel attention mechanism, thus obtaining a better localizing performance. The residual blocks are introduced to extract deeper features, which can stack more layers for high-level features and avoid gradient vanishing or exploding at the same time. The attention mechanism is taken into account for the feature extraction stage in the proposed SSL model, which can focus on the most important information on the input features. We use the signals collected by microphone array to explore the performance of the model under different features, and find the most suitable input features of the proposed method. We compare our method with other models on public dataset. Experience results show a quite substantial improvement of sound source localizing performance. [ABSTRACT FROM AUTHOR]

Beamforming results depend on the spatial resolution of the microphone array used, which may lead to sources close to each other being considered as one. Deconvolution methods that consider all directions simultaneously, such as DAMAS, produce better results in these situations. However, they have a high computational cost, often lack sufficient speed to be used in real-time applications, and have limited accuracy at lower frequencies. This paper introduces a hybrid method to perform deconvolution using a neural network that can improve the speed of deconvolution on high-resolution grids by more than 2 orders of magnitude, while also generating sparser maps without sacrificing accuracy compared to the compressed DAMAS method. [ABSTRACT FROM AUTHOR]

This paper describes a room response equalization technique based on an underdetermined multichannel inverse filtering (UMIF) and linearly constrained minimum variance (LCMV) approach. Not limited to the local control at the neighborhood of the measured control points, the proposed UMIF-LCMV system is capable of widening the effective equalization area of the reproduced sound field, with a large number of interpolated control points. Specifically, a constrained optimization problem is formulated to minimize the matching error at the interpolated control points while seeking precise matching at the measured control points. In practical implementation, only the frequency responses (FRs) associated with a limited number of control points need to be measured, whereas the FRs for the interpolated points are established by using a plane wave decomposition-based sensor interpolation technique. A two-stage procedure is developed to trim down plane wave components by using the least absolute shrinkage and selection operator (LASSO) algorithm and to obtain the complex amplitudes of the principal components. Simulations and objective and subjective experiments are conducted for a system comprising a linear loudspeaker array and a linear microphone array. The results have confirmed the efficacy of the proposed system in widening the effective listening area with only limited discrete measurements. [ABSTRACT FROM AUTHOR]

In this paper, we present an azimuth and elevation constant-beamwidth (CB) differential beamforming approach for cube arrays. We decompose a global cube beamformer into a Kronecker-product (KP) of three sub-beamformers: two constant-beamwidth beamformers along the y and z axes, and a tunable super-directive (SD) beamformer along the x -axis. We propose two design methods to derive cube beamformers whose either white noise gain (WNG) or directivity factor (DF) may be set by design. We show that the CB threshold frequency with respect to the azimuth and elevation angles, and the WNG and DF performance, can be controlled by the number of microphones along each axis. In addition, we focus on the particular case of merely a single microphone along the x -axis, which yields a rectangular azimuth and elevation CB beamformer. We demonstrate that its CB threshold frequencies are controlled by the number of microphones along the y and z axes, and show that the WNG and DF performance measures are maximized when the two axes are equal in size. Finally, we analyze the performance of the proposed beamformers through simulations of speech signals in various reverberant scenarios, including deviations in the desired speech signal's direction of arrival (DOA). We show that the proposed beamformers outperform previously-presented beamformers and the traditional SD and delay-and-sum (DS) beamformers, particularly in terms of the intelligibility of their corresponding time-domain enhanced signals. • We introduce an azimuth and elevation CB differential beamforming approach. • We design a cube beamformer as a KP of two CB beamformers and an SD beamformer. • Two derivation methods are proposed; in each, either the WNG or DF is set by design. • The CB threshold frequency and the WNG and DF are controlled by array axes' lengths. • The proposed approach outperforms previous approaches in noisy reverberant scenarios. [ABSTRACT FROM AUTHOR]

This study is the first to quantitatively measure the courtship display flights of Latham's snipe (Gallinago hardwickii), which is a "near threatened" species as of 2022 (IUCN red list of threatened species). By using a 16‐channel microphone array and 8‐channel microphone arrays, we localized the fine‐scale movements of courtship flights of one male performing at high altitude and high speed, and we estimated the direction from which each sound arrived using robot audition. Preliminary analyses of the azimuthal and elevation angles of the courtship flights partially revealed a fine‐scale flight trajectory. First, a male Latham's snipe gradually gained altitude while vocalizing sharp and harsh repeating calls, until it reached the flight peak altitude, then dove down while producing winnowing sound to the ground along the wetland zones without tall vegetation. This observation method is methodologically useful to establish a better understanding of Latham's snipe courtship flight site selection. Furthermore, this method can be extended to investigate other rare nocturnal or crepuscular birds that are too timid to risk ringing or tagging. [ABSTRACT FROM AUTHOR]

Abstrac: A method has been developed for determining the modal composition of correlated sound modes propagating in a cylindrical duct, the peculiarity of which is that the effect of sound reflection from the open end of the duct is taken into account by numerical or analytical calculation of the reflection coefficients. This method was validated in the TsAGI AC-2 anechoic chamber, in which the sound field was created in the duct of a small-scale air intake model using 12 speakers at fixed frequencies; the modal composition was determined using 48 microphones installed flush with the duct walls, and synchronous measurements of the directivity of sound emission from the open end of the duct in the far field were carried out. In this case, on based on the found amplitudes of sound modes in the duct, the radiation characteristics in the far field were calculated using an analytical solution for sound emission from a semi-infinite cylindrical duct, the results of which were compared with the measurement results. It is shown that the amplitudes of sound modes found according to the developed method make it possible to reconstruct the far sound field characteristics with high accuracy in cases where the number of generated propagating modes in the duct does not exceed the number of installed microphones in the cylindrical array. [ABSTRACT FROM AUTHOR]

A method has been developed for tuning the microphone array for optimal visualization of acoustic dipoles. Acoustic dipoles were generated under experimental conditions in an anechoic chamber using a subsonic air flow around a thin metal rod and thin rectangular plate. To confirm the operability of this approach, we compared the visualization maps obtained using the approach with the results obtained with a similar array class tuned in the traditional monopole method. The developed approach, which adapts the array to the dipole radiation model and modifies the transfer functions that take into account the influence of the incidence pattern of sound waves, demonstrates an increase in the array's dynamic range with optimal microphone placement. [ABSTRACT FROM AUTHOR]

The differential microphone array, or differential beamformer, has attracted much attention for its frequency-invariant beampattern, high directivity factor and compact size. In this work, the design of differential beamformers with small inter-element spacing planar microphone arrays is concerned. In order to exactly control the main lobe beamwidth and sidelobe level and obtain minimum main lobe beamwidth with a given sidelobe level, we design the desired beampattern by applying the Chebyshev polynomials at first, via exploiting the structure of the frequency-independent beampattern of a theoretical Nth-order differential beamformer. Next, the so-called null constrained and least square beamformers, which can obtain approximately frequency-invariant beampattern at relatively low frequencies and can be steered to any direction without beampattern distortion, are proposed based on planar microphone arrays to approximate the designed desired beampatterns. Then, for dealing with the white noise amplification at low-frequency bands and beampattern divergence problems at high-frequency bands of the null constrained and least square beamformers, the so-called minimum norm and combined solutions are deduced, which can compromise among the white noise gain, directivity factor and beampattern distortion flexibly. Preliminary simulation results illustrate the properties and advantages of the proposed differential beamformers. [ABSTRACT FROM AUTHOR]

Aiming at the problem of poor spatial resolution of low-frequency noise sources in a small-aperture spherical microphone array (SMA), this paper proposes a method for localizing and identifying low-frequency noise sources based on virtual-vector open SMA ('p+v' joint processing method of pressure and velocity). Firstly, a virtual open SMA with a larger aperture is obtained using a virtual array extrapolation method. In this method, the virtual SMA and the actual SMA are regarded as a dual-radius SMA, and velocity information is obtained using finite difference elements of the same direction (azimuth and elevation) array of the virtual and actual SMA. At the same time, the sound pressure at the velocity position is obtained using the virtual SMA extrapolation method and the virtual vector array element SMA, whereby both velocity and sound pressure information is obtained. Finally, the vector signal processing technology is introduced into the generalized inverse beamforming algorithm (GIB). After determining the vector transfer function of the 'p+v' joint processing mode, a low-frequency-noise-source localization and identification method based on the vector signal processing GIB is proposed. The simulation and experiment results show that a virtual SMA with a large aperture can be obtained using a virtual array extrapolation method, and the GIB with sound pressure and velocity joint processing has a better spatial resolution. [ABSTRACT FROM AUTHOR]

This study proposes a high-efficiency method using a co-prime circular microphone array (CPCMA) for the bearing fault diagnosis, and discusses the acoustic characteristics of three fault-type signals at different rotation speeds. Due to the close positions of various bearing components, radiation sounds are seriously mixed, and it is challenging to separate the fault features. Direction-of-arrival (DOA) estimation can be used to suppress noise and directionally enhance sound sources of interest; however, classical array configurations usually require a large number of microphones to achieve high accuracy. To address this, a CPCMA is introduced to raise the array's degrees of freedom in order to reduce the dependence on the microphone numbers and computation complexity. The estimation of signal parameters via rotational invariance techniques (ESPRIT) applied to a CPCMA can quickly figure out the DOA estimation without any prior knowledge. By using the techniques above, a sound source motion-tracking diagnosis method is proposed according to the movement characteristics of impact sound sources for each fault type. Additionally, more precise frequency spectra are obtained, which are used in combination to determine the fault types and locations. [ABSTRACT FROM AUTHOR]

Songbirds are one of the study targets for both bioacoustic and ecoacoustic research. In this paper, we discuss the applicability of robot audition techniques to understand the dynamics of forest bird vocalizations in a soundscape measured in azimuth and elevation angles with a single 16-channel microphone array, using HARK and HARKBird. First, we evaluated the accuracy in estimating the azimuth and elevation angles of bird vocalizations replayed from a loudspeaker on a tree, 6.55 m above the height of the array, from different horizontal distances in a forest. The results showed that the localization error of azimuth and elevation angle was equal to or less than 5 degrees and 15 degrees, respectively, in most of cases when the horizontal distance from the array was equal to or less than 35 m. We then conducted a field observation of vocalizations to monitor birds in a forest. The results showed that the system can successfully detect how birds use the soundscape horizontally and vertically. This can contribute to bioacoustic and ecoacoustic research, including behavioral observations and study of biodiversity. [ABSTRACT FROM AUTHOR]

The conventional equivalent source method for near-field acoustic holography is an effective noise diagnosis method using microphone array. However, its performance is limited by microphone spacing, so the effect is unsatisfied when the wave number is high. In this paper, to broaden the frequency suitability and improve the performance of sound source reconstruction with low signal-to-noise ratios, a block Bayesian compressive sensing method based on the equivalent source method is proposed. Numerical results show that this proposed method has a good reconstruction performance and makes wideband reconstruction possible. By changing the frequency, location, and signal-to-noise ratio of the sound source, the reconstruction performance of the proposed method can remain stable. Finally, the validity and practicability of the proposed method are verified by experiments. [ABSTRACT FROM AUTHOR]

A three-microphone acoustic array (OSU1), with microphones that have a flat response from 0.1 to 200 Hz, was deployed for 6 years (2016–2022) at Oklahoma State University (OSU) in Stillwater, Oklahoma, and sampled at 1000 Hz. This study presents a new dataset of acoustic measurements in a high interest region (e.g., study of tornado infrasound), provides a broad overview of acoustic detections and the means to identify them, and provides access to these recordings to the broader scientific community. A wide variety of infrasound and low-audible sources were identified and characterized via analysis of time traces, power spectral densities, spectrograms, and beamforming. Low, median, and high noise models were compared with global noise models. Detected sources investigated include natural (microbaroms, bolides, earthquakes, and tornadoes) and anthropomorphic (fireworks, airplanes, and munition detonations) phenomena. Microbarom detections showed consistency with literature (~0.2 Hz with peak amplitude in the winter) and evidence that the frequency was inversely related to the amplitude. Fireworks and airplanes served as verified local events for the evaluation of data quality and processing procedures. Infrasound from munition detonations, that occur nearly daily at a location 180 km southeast of OSU1, matched the available ground truth on days with favorable propagation to OSU1. A clear bolide detection with an estimated position of approximately 300 km from OSU1 was shown. Most detected earthquakes were seismic arrivals due to sensor vibrations; however, the largest earthquake in Oklahoma history showed an acoustic arrival. Finally, data from multiple tornadoes are discussed, including a previously unpublished quasi-linear convective system tornado. [ABSTRACT FROM AUTHOR]

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The use of machine learning in automatic speaker identification and localization systems has recently seen significant advances. However, this progress comes at the cost of using complex models, computations, and increasing the number of microphone arrays and training data. Therefore, in this work, we propose a new end-to-end identification and localization model based on a simple fully connected deep neural network (FC-DNN) and just two input microphones. This model can jointly or separately localize and identify an active speaker with high accuracy in single and multi-speaker scenarios by exploiting a new data augmentation approach. In this regard, we propose using a novel Mel Frequency Cepstral Coefficients (MFCC) based feature called Shuffled MFCC (SHMFCC) and its variant Difference Shuffled MFCC (DSHMFCC). In order to test our approach, we analyzed the performance of the identification and localization proposed model on the new features at different noise and reverberation conditions for single and multi-speaker scenarios. The results show that our approach achieves high accuracy in these scenarios, outperforms the baseline and conventional methods, and achieves robustness even with small-sized training data. [ABSTRACT FROM AUTHOR]

The planar microphone array's two-dimensional DOA compression beamforming technology offers wide recognition space and clear imaging, holding significant promise for various applications. However, early versions of compressed beamforming encountered issues with grid mismatch, where the true source DOA did not fall on the discrete grid. In this paper, a broadband off-grid DOA compressive beamforming based on block sparse Bayesian learning (BSBL) is proposed. We adopt a block sparse signal reconstruction model to address off-grid DOA compensation, further extending it to a broadband multi-snapshot model for enhanced noise robustness and universality. Leveraging the BSBL algorithm based on fast marginal likelihood maximization (FMLM), we efficiently solve the above model, achieving rapid broadband multi-snapshot off-grid compressive beamforming. Simulation and experimental findings demonstrate the method's superior spatial resolution, effectively mitigating basis mismatch, while multiple snapshots efficiently reduce noise interference in localization. • A broadband 2D off-grid compressive beamforming algorithm using fast block sparse Bayesian learning is proposed. • The computation time of the multi-snapshot block sparse Bayesian algorithm is less affected by the number of snapshots. • This method achieves more accurate source intensity, sparser imaging and lower time cost than the interior point method. [ABSTRACT FROM AUTHOR]

• A method is proposed to identify rotating sound sources in a cylindrical duct. • The proposed method overcomes the limitation of equidistant microphones. • More than half of microphones are remarkably saved compared to current methods. • The method has great prospect on measurement and localization of fan noise sources. The Doppler effect caused by the rotation of turbine fans brings significant challenges to the identification of fan noise sources inside the nacelle. Current methods of rotating source localization inside the cylindrical duct are executed with tough requirements for the microphone array mounted on the duct wall, which includes a fair enough number of microphones and equal-space distribution of each microphone in the azimuthal direction. A methodology based on nonuniform measurements and duct spin modes superposition (NMDMS) is proposed to identify rotating sources with high spatial resolution and few side lobes even near cut-on frequencies, which requires much fewer microphones and no uniform distribution of each microphone in the circumference. The sparsity in the azimuthal domain of the duct field generated by multiple rotating sources is verified theoretically, after which the azimuthal mode in each duct cross-section is reconstructed by the inhomogeneous measurements through the orthogonal matching pursuit (OMP) method. Followed by the identification of mode amplitude in the rotation frame, the sound pressure distribution as well as the axial acoustic velocity distribution on the source plane is reconstructed through duct modes summation. Numerical simulations and experiments are implemented to validate the method. Localization results indicate that rotating sources identification using the proposed method could not only save more than half the microphones without requirements of equal-space distribution but obtain good accuracy of localization and remarkably fewer side lobes. [ABSTRACT FROM AUTHOR]

The target beampattern of differential microphone arrays (DMAs) often satisfies design requirements to optimize the performance of the beamformer by maximizing a specific advantage. This paper focuses on designing and implementing the minimum mainlobe width beampattern under the constrained sidelobe level. The main works are as follows. (1) We derive the minimum mainlobe width target beampattern under certain sidelobe level constraints from the Chebyshev-Type pattern that satisfies the sufficient conditions for effective target beampatterns. (2) We design a Jacobi–Anger expansion approximation differential beamforming filter for the Chebyshev-Type target beampattern to ensure that the resulting beampattern is consistent with the Chebyshev-type target beampattern and the beamformer's robustness can be improved by using more microphones to obtain a minimum-norm solution. Compared with the conventional frequency-independent pattern Jacobi–Anger expansion method, the Chebyshev-Type Jacobi–Anger expansion beamformer we designed can flexibly limit the sidelobe level and obtain the minimum mainlobe beamwidth. • Design differential microphone array beampatterns with adjustable sidelobe levels. • Achieve the minimum mainlobe width for specified sidelobe level in beampatterns. • Address the trade-off between sidelobe levels and mainlobe width in differential beampattern design. • Obtain the desired directivity factor or maximize it by adjusting sidelobe level settings in the beampatterns. [ABSTRACT FROM AUTHOR]

Harmonic noise frequently arouses by the disturbances in industrial applications, which would be a great threat to the security, stability and service life of equipment in some large and critical facilities, especially in power systems. Therefore, finding a way to resist harmonic noise is highly important. The zeroing neural networks (ZNN) have lately gained exceptional success in solving time-varying problems (TVP) as a result of its efficiency. Inspired by the effectiveness of ZNN and the dynamic system model design principles in control theory, we initially develop a coupled anti-mixed noise ZNN (AMNZNN) model that can resist the combination of single harmonic and non-harmonic noise (e.g., random noise). Then, an extended AMZNN model is further designed to remove the combination of multi-harmonic noise and non-harmonic noise. Additionally, comparisons among original ZNN (OZNN), integration-enhanced ZNN (IEZNN), harmonic-noise-tolerant ZNN (HNTZNN) and the proposed AMNZNN for time-varying matrix inversion (TVMI) under the mixture of harmonic noise and random noise are experimented to demonstrate the proposed AMNZNN model's superior ability in resisting mixed noise. Finally, by applying the proposed extended formalism to power systems and microphone arrays in denoising, the effectiveness of the proposed method to resist multi-harmonic and random noises is further verified in scientific applications. • A coupled AMNZNN is proposed for solving TVP with case of the existing mixed noises at the same time. • The convergence performance of AMNZNN model is guaranteed theoretically. • The simulative experiments show that AMNZNN model has good performance in anti-mixed noise. [ABSTRACT FROM AUTHOR]

• A non-conventional array structure − the Fibonacci sphere algorithm – was selected. • This network structure enables continuous spatial sound localization without additional grid segmentation. • The method's localization capability after removing array elements was investigated. • Good spatial sound source localization results were obtained through a neural network. This paper proposes a novel Sound Source Localization (SSL) algorithm based on neural networks in the time domain space. Building upon previous research [Tang J, Sun X, Yan L, et al. Sound source localization method based time-domain signal feature using deep learning. Appl Acoust 2023;213:109626] that leverages neural network techniques for sound source localization, our methodology diverges from conventional grid-based approaches by circumventing spatial resolution limitations inherent to meshing through direct prediction of target coordinates via a regression method. We employ the Fibonacci Sphere Algorithm (FSA) to ensure a uniform distribution of microphone array elements, enhancing the array's response consistency to sound sources from various directions. Our comprehensive model simulates a spatial SSL system within a 10-m spherical space. Experimental investigations have substantiated that the proposed neural network architecture demonstrates exceptional localization precision, as evidenced by the Mean Absolute Errors (MAE) obtained, which are 0.268, 0.304, and 0.287, correspondingly, when applied to 64-, 32-, and 16-element array configurations. Furthermore, our experiments demonstrate the strong generalization capability of the trained models, maintaining satisfactory performance even with element losses ranging from 5 % to 30 %. These findings highlight the potential of neural networks in SSL applications and provide valuable insights for future research and development in this field. [ABSTRACT FROM AUTHOR]

For environments where the sound source is located directly behind an obstacle, this work proposes a non-line-of-sight sound source localization algorithm based on fast marginalized block sparse Bayesian learning (BSBL-FM) and second-order edge diffraction. The second-order edge diffraction transfer function is calculated using the Biot-Tolstoy-Medwin method and used to construct the sensing matrix. By leveraging the spatial sparsity of the sound source signal, a block sparse measurement model is formulated, and BSBL-FM is applied for sparse reconstruction to achieve high-resolution localization. Simulation results demonstrate that the proposed algorithm accurately locates the source position and identifies the source strength, providing higher spatial resolution than beamforming and more precise source strength identification than matched-field processing (MFP). Experimental results validate the simulation findings and further show that the proposed algorithm achieves smaller localization errors than both beamforming and MFP. • A non-line-of-sight sound source localization algorithm is proposed. • It can accurately locate the sound source position while accurately identifying the sound source strength. • It provides higher spatial resolution than beamforming and achieves smaller localization errors than beamforming and MFP. [ABSTRACT FROM AUTHOR]

In scenes with noise and overlapping speakers, directionally extracting audio tracks corresponding to individual speakers is crucial for immersive and interactive spatial audio systems. Although neural networks have been successful in this task, existing steering approaches for adjusting the direction of neural speech extraction mainly target spatial audio directly collected by microphone arrays, while directional speech extraction with Ambisonics spatial audio is less well studied. Therefore, to encode the target directional information as input for the neural network, this paper proposes two Ambisonics directional features based on the spatial feature difference and beamforming principle: the relative harmonic difference and the directional signal enhancement ratio. Using the special property of Ambisonics' rotation transform, a rotary steering pre-processing is also proposed to align the target speaker's direction with a fixed reference by inversely rotating the sound field, thereby simplifying multi-directional extraction to fixed-directional extraction. Finally, we integrate these proposed approaches with the existing temporal-spectral-spatial filtering neural networks to establish a generalized framework for steerable speech extraction and conduct experiments on a simulated Ambisonics dataset containing multiple speakers and noise sources. The experiments show that the proposed approaches outperform existing conditional steering and can be applied to various existing neural network architectures. • This work focuses on steering approach in the Ambisonics neural directional speech extraction. • We propose two Ambisonics directional features. • We propose a rotary steering pre-processing method for Ambisonics signals. • The proposed steering approaches demonstrate performance improvement in the Ambisonics neural directional speech extraction. [ABSTRACT FROM AUTHOR]

Fly Ormia ochracea ears–inspired MEMS directional microphones show directionality at a reduced size. The progress within this area is limited to prototype development and feasibility studies, which leaves a limited choice of using it in practical applications. Here, we presented how this trend of directional microphones reacts to multiple incoming sound sources and what we can hear after converting the sensing sound. A multi-sound receiver is developed that receives multiple incoming sounds, and an analog logic is added to differentiate them. When the intensity, frequency, and propagation angle are unknown, the developed device is evaluated in an anechoic chamber using numerous sound sources. The results are promising and enable the advances of using fly Ormia ochracea 's ears-inspired MEMS directional microphone in various acoustic applications.   • A uniquely designed bio-inspired MEMS directional microphone array is introduced. • The microphone array senses multiple sound sources simultaneously. • An in-house designed circuit processes multiple sound sources, identifying the sound of interest. • The developed approach is experimentally demonstrated as a multi-sound receiver. [ABSTRACT FROM AUTHOR]

Beamforming acoustic imaging technology, utilizing phased microphone arrays, enables precise sound source localization and finds widespread application in aerodynamic wind tunnel testing, acoustic signal recognition, and mechanical fault diagnosis. This paper presents a comprehensive review of beamforming evolution, detailing its mathematical foundations and diverse applications in acoustic imaging. Various beamforming methodologies are critically analyzed using wind tunnel test data, and an overview of correction methods for external interferences and array optimization approaches is provided. Through this examination, the strengths and limitations of each method are highlighted, offering insights for future research. Additionally, potential future enhancements, including paradigm-shift approaches to advance beamforming capabilities, are explored, suggesting directions for further innovation. This review aims to establish a foundation for newcomers to the field, stimulate academic discussion, and drive ongoing research in acoustic imaging. By elucidating beamforming complexities, correction methods, and optimization techniques, this study seeks to enhance collective knowledge and support continued advancements in this technology. [ABSTRACT FROM AUTHOR]

The conventional post-filtering methods in the multi-channel noise reduction application only rely on multi-channel input signals while neglecting the noise reduction capability of the microphone array beamformer, which results in overestimation of the noise power spectral density (PSD) and consequently suboptimal filters in the minimum mean-square-error (MMSE) sense. This paper proposes a novel microphone array post-filter based on accurate PSD estimation, the beamformer output in the microphone array is used to estimate the noise PSD, and a two-step noise reduction method is also employed to obtain accurate post-filter gain function. The error analysis is also given to highlight the advantage of the proposed algorithm over the conventional Zelinski and McCowan post-filters. The performance advantages of the proposed post-filter are demonstrated in terms of segmental SNR (SegSNR), short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and deep noise suppression mean opinion score (DNSMOS). • A microphone array post-filter based on accurate PSD estimation is proposed. • Mathematical derivations and simulations analyze the error between the proposed and conventional methods. • Simulations and experiments verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

• An acoustic-based fault diagnosis system is proposed to accurately diagnose and orientate acoustic sources under low signal-to-noise ratios. • A multi-task TCN model is proposed to simultaneously calculate the orientation and the fault type of the faulty acoustic source. • An attentional mechanism that combines information from the sound field into the separation of fault sources is proposed. Planetary gearboxes are vital in industrial production due to their large transmission ratios. Therefore, accurate fault diagnosis of planetary gearboxes is crucial. However, in industrial applications, the acoustic fault signals from two different adjacent planetary gearboxes may overlap and interfere with each other, resulting in a low Signal-to-Noise Ratio (SNR) for each acoustic fault source, which in turn prevents accurate fault diagnosis. In this context, the Multi-Task Learning-Temporal Convolutional Network (MTL-TCN) is proposed to simultaneously output the orientation of the acoustic sources as well as the fault type to solve the problem of interference between adjacent acoustic sources. A Spatial information based Multi-Task Channel Attention (SMTCA) mechanism is also proposed to solve the problem of acoustic signal overlapping by using the orientation information to calculate the weight of the acoustic signal channel and assigning it to the fault diagnostic task, which combines the sound field information into the separation of fault sources. Finally, a Multi-Channel Acoustic based diagnose System (MC-ABDS) is proposed, which contains a customized microphone array as well as a sound field information and fault feature information extraction method called Multi-Task Attention TCN (MTA-TCN). The system is validated by the data collected in the anechoic chamber, and it is effective for the acoustic overlapping and interference that occurs when two adjacent planetary gearboxes are operating. The orientation accuracy of the acoustic source reached 99.98 %and the diagnostic accuracy of the fault reached 92.08 %. [ABSTRACT FROM AUTHOR]

Wireless acoustic sensor network (WASN) has shown great potential for speech enhancement compared with traditional microphone arrays. As dealing with all sensor measurements demands a powerful centralized processor, a distributed framework for speech enhancement is developed in this work. We propose to suppress noises by jointly exploiting the temporal and spatial information of audio signals, based on the information weighted consensus (IWC) filtering. In order to reduce the computational cost as well as the transmission power, we activate only an informative subnetwork by maximizing the energy efficiency in the WASN, where the subnetwork connectivity is guaranteed by incorporating more constraints. To select sensors rapidly, we propose a near-optimal greedy method, which removes one sensor at each iteration until the subnetwork size equals a preset value. The proposed method can obtain the high-quality target signal in a distributed and energy-efficient way. Simulation and real-world experiment results confirm its validity. • A distributed frame-work for speech enhancement via temporal and spatial data fusion. • Sensor selection strategy to reduce power consumption in system. • A near-optimal greedy searching method for sensor selection. [ABSTRACT FROM AUTHOR]

Deep learning based sound source localization is a growing research topic for wireless acoustic sensor networks. However, current methods just combine the DOA estimates provided by each microphone array node or use end-to-end architecture with multi-channel features of the arrays. The above methods suffer from performance degradation in high noise and reverberation environments. In this paper, we propose a deep learning-based method using spatial spectrum features and attention mechanisms to estimate the locations of sound sources. We first propose a new set of features to represent the spatial information in multiple frequency bands. By using sub-band spatial representations, the model can adequately utilize the geometric properties and the spatial spectrum of the array nodes. Then we propose to use a CNN-Transformer-based network to identify the correct peaks and suppress spurious peaks by modeling both local and global information from the spatial spectrum features. To evaluate the proposed method, we perform experiments using simulated datasets with different noises and reverberations, as well as different arrays and source positions. Experimental results show that the proposed method achieves the lowest RMSE and the highest F1 score compared with baseline methods. Further analysis demonstrates that the proposed method has robust sound sources localization performance in simulations and real data experiments. [ABSTRACT FROM AUTHOR]

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.

Abstract In recent years, spatial audio reproduction has been widely researched with many studies focusing on headphone-based spatial reproduction. A popular format for spatial audio is higher order Ambisonics (HOA), where a spherical microphone array is typically used to obtain the HOA signals. When a spherical array is not available, beamforming-based binaural reproduction (BFBR) can be used, where signals are captured with arrays of a general configuration. While shown to be useful, no comprehensive studies of BFBR have been presented and so its limitations and other design aspects are not well understood. This paper takes an initial step towards developing a theory for BFBR and develops guidelines for selecting the number of beamformers. In particular, the average directivity factor of the microphone array is proposed as a measure for supporting this selection. The effect of head-related transfer function (HRTF) order truncation that occurs when using too many beamformer directions is presented and studied. In addition, the relation between HOA-based binaural reproduction and BFBR is discussed through analysis based on a spherical array. A simulation study is then presented, based on both a spherical and a planar array, demonstrating the proposed guidelines. A listening test verifies the perceptual attributes of the methods presented in this study. These results can be used for more informed beamformer design for BFBR.

In the infrasonic observation of a fumarolic field, distinguishing multiple fumarolic sources is challenging. The array technique effectively estimates the source locations and identifies the target signal from other signals and noise. We conducted an experiment at Kirishima Iwo-Yama, Japan, where two active fumarolic areas were separated by ~ 450 m. A three-element array with an aperture of ~ 20 m was installed between the two fumarolic areas. In addition, a single microphone was installed near one of the fumaroles. The array combined with the waveform correlation analysis estimated the most prominent source but failed to estimate the other weak source. A joint analysis of the array and the single microphone effectively resolved the two sources. It was also confirmed that newly developed power-saving MEMS microphones were useful for observing the fumaroles. This paper presents the instrumentation and analytical method that would be beneficial for monitoring volcanoes that have multiple hydrothermally active vents. [ABSTRACT FROM AUTHOR]

(1) Background: Transition to smart cities involves many actions in different fields of activity, such as economy, environment, energy, government, education, living and health, safety and security, and mobility. Environment and mobility are very important in terms of ensuring a good living in urban areas. Considering such arguments, this paper proposes monitoring and mapping of a 3D traffic-generated urban noise emissions using a simple, UAV-based, and low-cost solution. (2) Methods: The collection of relevant sound recordings is performed via a UAV-borne set of microphones, designed in a specific array configuration. Post-measurement data processing is performed to filter unwanted sound and vibrations produced by the UAV rotors. Collected noise information is location- and altitude-labeled to ensure a relevant 3D profile of data. (3) Results: Field measurements of sound levels in different directions and altitudes are presented in the paperwork. (4) Conclusions: The solution of employing UAV for environmental noise mapping results in being minimally invasive, low-cost, and effective in terms of rapidly producing environmental noise pollution maps for reports and future improvements in road infrastructure. [ABSTRACT FROM AUTHOR]