Acoustic Beamforming Research Articles

Acoustic beamforming using maximum likelihood criteria with Laplacian model of the desired signal

Acoustic beamforming is a technique used in audio engineering and signal processing to filter sound waves in the spatial domain. Usually, an array of microphones is used to capture sound from different directions. These array signals are then combined to reinforce each other in the desired direction while cancelling the noise or interference from other directions. An important application of acoustic beamforming is the cocktail party scenario where multiple people speak simultaneously in a noisy room. To capture the speech of only the desired speaker, acoustic beamforming is used. Usually, in such cases, the target speech is modelled as a zero-mean complex Gaussian distributed random variable. However, the target speech coefficients are sparse in the time-frequency domain. Hence, in our work, we model the speech coefficients using a zero-mean circular Laplacian distribution. After modelling the target speech, we formulate a beamformer based on the maximum likelihood criteria. We add a distortionless constraint to the proposed beamformer to further improve performance. The final solution of the proposed beamformer encourages sparsity, indicating that it models the target speech better than the complex Gaussian distribution. Simulations show the effectiveness of the proposed beamformer in capturing the target speech and rejecting interfering speakers.

Development of an experimental technique for measuring polar diagrams of adaptive directivity implemented in hearing aids

Nowadays, hearing aids have efficient signal processing options to improve speech intelligibility in noise. This work concerns the characterization of the microphone systems directivity available in hearing aids. In recent decades, "adaptive" directivities have been implemented in hearing aids, thus making it possible to focus on useful sound sources in the hearing aid patient environment. These options use the principles of sound environments detection, acoustic beamforming and noise reduction. Performances of these options can be estimated with subjective methods (as speech audiometry in noise), and objective methods (listening effort, intelligibility indicators as HASPI, SII, STI, etc.). But these methods provide an overall assessment of all the processing involved in speech enhancement and are not designed to characterize the effects of adaptive directivity. We developed an experimental technique based on sound sources separation with phase opposition diffusion (known as "Hagerman" method) to draw polar diagrams of adaptive directivity, for realistic sound environments. We used a sound multi-diffusion system to generate various sound environments with noise and speech sources around an artificial head equipped with hearing aids. The first results show the adaptive directivity effects associated with noise reduction provided by the latest hearing aids generation on everyday hearing scenes.

Soft bio-metamaterials with high acoustic transparency and gradient refractive index for tunable acoustic beamformer

Soft bio-metamaterials with high acoustic transparency and gradient refractive index for tunable acoustic beamformer

An application of a beamforming technique, linear acoustic array and robot for pipe condition localization

Pipe inspection robots with sensors significantly enhance the maintenance of water systems by enabling early defect detection and reducing the risks of leaks and environmental damage. This paper introduces a sparse representation acoustic beamformer designed to locate artefacts within pipes using a short linear microphone array on a robot. The primary contributions include: (i) a new acoustic beamforming approach based on sparse representation that accurately predicts pipe conditions with a localization error within 3 % of the sensing distance; (ii) an enhanced beamforming algorithm combining plane wave and first non-axisymmetric mode, extending the frequency range from < 1300 Hz to < 2200 Hz in a 150 mm diameter pipe (an increase by 1.69 times), effectively managing unwanted acoustic reverberations and distinguishing blockages from lateral connections; and (iii) a novel robust algorithm predicting pipe length with an error margin within 2 %. This research advances acoustic sensing technologies for autonomous mobile robots inspecting buried pipes.

Pass-by Noise Analysis of Heavy Duty Trucks

As part of the NCHRP Project 25-45, pass-by levels were measured for 1,289 heavy duty trucks operating on 20 sites including multi-lane highways and more rural two-lane roadways. These sites included uphill, downhill, level sites and accelerating and cruising conditions at speeds from below 45 mph to 70 mph as well as nine different pavement types. This research was designed to develop a more comprehensive understanding of truck noise emissions and noise sources. In this paper, the frequency spectra for these pass-bys are considered from 25 to 10,000 Hz under the measurement conditions specified in the AASHTO TP 389-20 procedure for statistical isolated pass-bys. These data are enhanced by results from the 2009 NCHRP Report 635 entitled Acoustic Beamforming: Mapping Sources of Truck Noise and from Caltrans research on truck tire noise. The findings of these various studies are presented in order to develop a more comprehensive description of heavy-duty truck noise emissions.

Acoustic Characterization of the NASA Langley 14- by 22-Foot Subsonic Tunnel using a Phased Array

A characterization test was conducted in the NASA Langley Research Center 14- by 22-Foot SubsonicTunnel to assess recent acoustic modifications in the facility. Test section reflectivity, backgroundnoise and shear layer effects were evaluated by taking measurements of known noise sources.Both conventional speakers and a high-pressure air source were employed as noise generationmechanisms. These were mounted to a pole or fairing at a consistent location in the test section.The source waveforms were used to represent the expected frequency content of scaled Urban AirMobility vehicles. Acoustic measurements were taken using two unique microphone arrays placed outside the core flow, one of which was a 54-element streamwise-traversing phased array. The other was an 11-element linear tower array. The applicability of phased array acoustic beamforming techniques to mitigate shear layer effects and reject background noise is presented. Additionally, beamforming results are compared to results from single-microphone processing techniques. Flow speeds investigated ranged from Mach 0.00 (static) to Mach 0.12. The tunnel has been partially acoustically treated but still exhibits flow noise, reducing the signal-to-noise ratio of the source under investigation. Conventional beamforming was able to capture high frequency content passing through the shear layer better than single microphone measurements. CLEAN deconvolution improves source mapping at low frequencies.

Development of an acoustic fault diagnosis system for UAV propeller blades

With the rapid growth in demand for unmanned aerial vehicles (UAVs), novel maintenance technologies are essential for ensuring automatic, safe, and reliable operations. This study compares two fault detection systems that utilize the acoustic signature of UAV propeller blades for classifying their health state. By employing an acoustic camera with 112 microphones for spatial resolution of sound sources, datasets of acoustic images are generated in three differently reverberating environments for the third octave frequency bands of 6300 Hz, 8000 Hz, 10,000 Hz and 12,500 Hz. A convolutional neural network (CNN) is trained and evaluated with maximum F1-scores of 0.9962 and 0.9745 for two and three propeller health classes, respectively. Furthermore, we propose a second approach based on a linear classification (LC), which utilizes a rotating beamformer for comparison. This approach uses only two sound sources that are identified after the acoustic beamforming of a two-bladed propeller. In comparison, this algorithm detects propeller tip damages without applying a machine learning algorithm and reaches a slightly lower F1-score of 0.9441.

Learning an interpretable end-to-end network for real-time acoustic beamforming

Recently, many forms of audio industrial applications, such as sound monitoring and source localization, have begun exploiting smart multi-modal devices equipped with a microphone array. Regrettably, model-based methods are often difficult to employ for such devices due to their high computational complexity, as well as the difficulty of appropriately selecting the user-determined parameters. As an alternative, one may use deep network-based methods, but these are often difficult to generalize, nor can they generate the desired beamforming map directly. In this paper, a computationally efficient acoustic beamforming algorithm is proposed, which may be unrolled to form a model-based deep learning network for real-time imaging, here termed the DAMAS-FISTA-Net. By exploiting the natural structure of an acoustic beamformer, the proposed network inherits the physical knowledge of the acoustic system, and thus learns the underlying physical properties of the propagation. As a result, all the network parameters may be learned end-to-end, guided by a model-based prior using back-propagation. Notably, the proposed network enables an excellent interpretability and the ability of being able to process the raw data directly. Extensive numerical experiments using both simulated and real-world data illustrate the preferable performance of the DAMAS-FISTA-Net as compared to alternative approaches.

A circular microphone array with virtual microphones based on acoustics-informed neural networks.

Acoustic beamforming aims to focus acoustic signals to a specific direction and suppress undesirable interferences from other directions. Despite its flexibility and steerability, beamforming with circular microphone arrays suffers from significant performance degradation at frequencies corresponding to zeros of the Bessel functions. To conquer this constraint, baffled or concentric circular microphone arrays have been studied; however, the former need a bulky baffle that interferes with the original sound field, whereas the latter require more microphones that increase the complexity and cost, both of which are undesirable in practical applications. To tackle this challenge, this paper proposes a circular microphone array equipped with virtual microphones, which resolves the performance degradation commonly associated with circular microphone arrays without resorting to physical modifications. The sound pressures at the virtual microphones are predicted from those measured by the physical microphones based on an acoustics-informed neural network, and then the sound pressures measured by the physical microphones and those predicted at the virtual microphones are integrated to design the beamformer. Experimental results demonstrate that the proposed approach not only eliminates the performance degradation but also suppresses spatial aliasing at high frequencies, thereby underscoring its promising potential.

Robustness analysis and experimental validation of a deep neural network for acoustic source imaging

Deep Neural Network (DNN) models offer an attractive alternative to existing acoustic source imaging techniques, such as acoustic beamforming, due to their ever-growing potential with increasing computational power. Source resolution of acoustic beamforming methods is limited at lower frequencies and their source maps may possess sidelobes at higher frequencies. However, acoustic beamforming methods are typically robust over a wide range of simulation and experimental conditions, such as (i) the number of sources present, (ii) source frequency and (ii) extraneous noise sources. The performance of DNN models, when these conditions are varied from their specific design criteria, is yet to be investigated and much work is needed in this area before DNN models can be utilized in experiments, such as wind tunnel tests. Furthermore, few studies have been conducted on experimental validation of DNN models, predominately due to the difficulty of large sets of experimentally obtained data needed for DNN model training and the sensitivity of DNN model performance when any of the aforementioned experimental conditions are varied. In this paper, a series of studies on the robustness of DNN models based on numerical data and experimental data are presented. Numerical DNN (NDNN) models are trained using in-phase and random-phase pressure data generated from six sources over design frequencies from 500 Hz to 20,000 Hz. The robustness of the NDNN models is tested via (1) inclusion of extraneous Gaussian white noise, (2) inclusion of extraneous tonal noise near the design frequency, (3) using source frequencies that slightly differ from the design frequencies and (4) using a number of sources that differs from the design source number. DNN model performance metrics are introduced that present a promising framework for future DNN model studies and bridging the gap between NDNN and experimentally trained DNN models. A preliminary experimental validation was conducted using a single speaker that was systematically placed over a speaker grid to generate training data via acoustic superposition, from which an experimentally trained DNN (EDNN) model was produced. The EDNN model yields exceptional noise source localization capability of the DNN model, revealing a promising start for a more sophisticated EDNN model.

Field Testing of an Acoustic Method for Locating Air Leakages in Building Envelopes

Maintaining the airtightness of building envelopes is critical to the energy efficiency of buildings, yet leak detection remains a significant challenge, particularly during building refurbishment. This study addresses the effectiveness of the acoustic beamforming measurement method in identifying leaks in building envelopes. For this reason, an in-field study employing the acoustic beamforming measurement method was conducted. The study involved testing over 30 rooms across three different multi-story office buildings of varying ages and heterogeneous envelope structures. Numerous leaks were located in the façades, which were subsequently visually confirmed or even verified with smoke sticks. The data, captured using an acoustic camera (a microphone ring array), revealed distinct spectra that indicate the method’s potential for further research. The basic functionality and the significant potential of this methodology for localizing leakages in large buildings were proven.

Relevance of quadrupolar sound diffraction on flow-induced noise from porous-coated cylinders

This paper elucidates the link between the near-wake development of a circular cylinder coated with a porous material in a low Mach-number flow and the related aerodynamic sound attenuation. It accomplishes this by formulating the cylinder flow-induced noise as a diffraction problem. The necessity for such an approach is driven by experimental evidence obtained through acoustic beamforming and particle-image-velocimetry measurements, which reveal that the dominant noise sources for a coated cylinder are not localised on the body surface but rather in the wake, specifically at the outbreak position of the shedding instability. The acoustic field at the vortex-shedding frequency can be hence modelled by considering a compact lateral quadrupole at this location and employing an exact Green’s function tailored to a cylindrical geometry. Because of the diffraction of the sound waves radiated by the quadrupolar source on the cylinder surface, the resulting far-field directivity pattern resembles that of a dipole. The study demonstrates that the porous coating has the two-fold effect of decreasing the strength of the point quadrupole in the wake and moving its origin further downstream, reducing, in turn, the efficiency of the sound scattering. Consequently, the diffracted part of the acoustic field, which dominates the far-field noise for a bare cylinder in accordance with classical theory, provides a contribution that is comparable to the direct part. The results eventually indicate that quadrupolar sources must be considered to accurately predict the noise radiated from a porous-coated cylinder, even at low Mach numbers.

Comparison of conventional and adaptive acoustic beamforming algorithms using a tetrahedral microphone array in noisy environments

In situ acoustic measurements are often plagued by interfering sound sources that occur within the measurement environment. Both adaptive and conventional beamforming algorithms, when applied to the outputs of a microphone array arranged in a tetrahedral geometry, are able to capture sound sources in desired directions and reject sound from unwanted directions. Adaptive algorithms may be able to measure a desired sound source with greater spatial precision, but require more calculations and, therefore, computational power. A conventional frequency-domain phase-shift algorithm and a modified adaptive frequency-domain Minimum Variance Distortionless Response (MVDR) algorithm were applied to simulated and recorded signals from a tetrahedral array of omnidirectional microphones. The algorithms are described mathematically and demonstrated on both deterministic and real-world sound data, to quantitatively validate and compare their performance and to provide listening examples of their outputs in a variety of acoustically replicated environments. [Work supported by Portland State University.]

Enhancing the noise reduction capability of the trailing edge serration at incidence using serration extension

This paper presents an experimental study to enhance the noise reduction capability of the trailing edge serration by shifting the serration root downstream of the trailing edge. The distance between the serration root and the trailing edge is referred to as extension length in this study. Three different extension lengths of 5 mm, 10 mm, and 15 mm are employed. Experiments were performed on a 100 mm chord NACA 0012 wing model with sawtooth trailing edge serration. The acoustic beamforming was performed for various angles of attack and flow speeds between 20 m/s and 50 m/s corresponding to chord-based Reynolds number of 1.4 × 10^5 and 3.5 × 10^5 respectively. Results show that the extension can improve the noise reduction capability of the trailing edge serration, especially for the flapped serration it can reduce the broadband noise by up to 15 dB at 8.5 deg. angle of attack. To understand the observed noise reduction, we employ the particle image velocimetry measurements of the wall-normal plane along the serration root. With extension a gradual reduction in the turbulent kinetic energy is observed for the flapped serration. Overall, the serration extension proves to be effective in reducing the trailing edge noise without aerodynamic penalty.

Designing of two-dimensional acoustic beamforming array using machine learning

Acoustic beamforming arrays are used for locating sound sources and their performance is measured by their directivity pattern. Designing an acoustic beamforming array to meet specific performance requirements can be challenging and requires numerical optimization of various factors such as array width, microphone positions, and the number of microphones. This study employed a Deep Neural Network (DNN) approach to predict the arrangement of a two-dimensional acoustic beamforming array giving the desired beam pattern. The DNN was trained on 90,000 microphone array designs and beam patterns of 1-kHz sound. The prediction accuracy was evaluated based on the mean absolute errors (MAE) of beam patterns, frequency-averaged main lobe width (MLW), and maximum sidelobe level (MSL). The results for commonly designed arrays datasets such as Brüel &amp; Kjær style array had a 26.54% error in beam patterns, 2.34 degree/m error in MLW, and 4.21 dB error in MSL. However, for predicted arrays generated by the random array designs, the results were more accurate, with deviations of 22.08% in beam patterns, 1.82 degree/m error in MLW, and 5.07 dB in MSL.

High-resolution acoustic beamforming based on genetic algorithms

A beamforming technique based on the genetic algorithm is proposed in this paper. This method reconstructs acoustic sources by exploiting the sparsity. To reduce the computational cost and speed up the implementation, a highly efficient algorithm is further developed by narrowing down the search domain for the genetic algorithm. The performance is numerically investigated through a quantitative analysis of the position and amplitude error of the recovered source, and experimentally demonstrated in acoustic imaging. For this newly established approach, the results indicate a higher resolution capability than the conventional beamforming, and better accuracy and robustness than the compressive sensing beamforming. Overall, the present work can enrich existing high-resolution beamforming methods and benefit relevant acoustic testing applications.

Learning to sound imaging by a model-based interpretable network

Acoustic beamforming methods based on microphone arrays have been widely used for sound source localization in various industrial fields. The conventional methods such as Delay and Sum (DAS) beamforming are limited by poor spatial resolution although their computational complexity is low. Efforts have been made to improve the resolution of the beamforming map, achieved by both model-based and deep network-based approaches. However, the model-based methods usually suffer from high additional computational effort and largely rely on selecting user-determined parameters. The deep network-based methods may make it difficult to guarantee generalization as well as hard to generate the beamforming map directly. In this paper, we first propose a scheme DAMAS-FISTA-LASSO to solve the inverse problem of sound imaging, significantly reducing runtime requirements. By deep unfolding the proposed algorithm, we further have designed an end-to-end interpretable model-based deep neural network termed DAMAS-FISTA-LASSO-Net (DFLNet) for real-time and high-resolution mapping of acoustic sources, which could obtain satisfying performance on both simulated and real-world data, demonstrating the strong generalization capability of the network.

A comparison of background noise reduction algorithms for improving acoustic beamforming output

This work presents a comparison of different background noise reduction algorithms available in literature at a range of signal-to-noise ratios (SNRs) for improving the conventional beamforming (CB) source maps. The algorithms include the classical background noise removal (BNR), eigenvalue identification organization and subtraction (EIOS), subspace-based background subtraction (SBS), and ensemble empirical mode decomposition (EEMD), amongst others. To assess the performance of different algorithms, three test-cases were considered: (1) localizing a loudspeaker placed in an anechoic environment in the presence of background white-noise, (2) diagnosing machine-tool noise source(s) during machining, i.e., when the workpiece and tool piece are in contact and (3) localizing airfoil trailing-edge (TE) noise sources in the presence of background tunnel noise. The analysis showed that the suitability of an algorithm depends upon the given situation – for the loudspeaker source, all algorithms deliver comparable results while for machine-tool application, the relatively simple BNR algorithm delivered the desired improvements in CB maps. For the experimental airfoil TE noise test-case, the EIOS and EEMD algorithms demonstrated substantial enhancements as compared to other methods. This investigation, therefore, suggests that for a given application, it is desirable to customize the background noise reduction algorithm to obtain optimal results.

Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers

This paper presents an approach to damage identification in beams by modal curvatures based on the use of beamforming algorithms. These processors have been successfully used in acoustics for the last thirty years to solve the inverse problems encountered in source recognition and image reconstruction, based on ultrasonic waves. In addition, beamformers apply to a broader range of problems in which the forward solutions are computable and measurable. This especially concerns the field of structural vibrations, where the use of such estimators has not received attention to date. In this paper, modal curvatures will play the role of replica vectors of the imaging field. The choice to use modal curvatures is motivated by means of numerical studies and experimental tests on a steel beam. Furthermore, we compare the performance of the Bartlett and minimum variance distortionless response (MVDR) beamformers with an estimator based on the simple minimization of the difference between model and measured data. The results suggest that the application of the MVDR beamformer is highly effective, especially in cases of slight damage between two sensors. MVDR enables both damage localization and quantification.

Experimental Study on Wind Turbine Airfoil Trailing Edge Noise Reduction Using Wavy Leading Edges

Aerodynamic noise produced by the rotating blade is an important hindrance for the rapid development of modern wind turbines. Among the various noise sources, the airfoil trailing edge noise contributes a lot to the wind turbine noise. The control of wind turbine airfoil trailing edge self-noise by bio-inspired sinusoidal wavy leading edges is experimentally studied in a semi-anechoic chamber. The noise radiated by the baseline NACA 0012 airfoil and various wavy airfoils is measured using a planar microphone array consisting of fifty-two microphones. The noise source identifications are achieved by using the CLEAN-SC method. The effects of velocity and angle of attack on noise radiation of the baseline airfoil are analyzed in detail. The noise control law of the wavy amplitude and wavelength on airfoil trailing edge noise is explored. Based on the acoustic beamforming results, the noise control effects of the wavy leading edges are intuitively demonstrated. In general, the wavy leading edge with a larger amplitude and smaller wavelength has a better effect on the airfoil trailing edge noise reduction. The maximum sound pressure level reduction can be up to 33.9 dB. The results of this study are expected to provide important information for wind turbine aerodynamic noise control.