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Virtual rotating array (VRA) beamforming is a robust technique in the identification of rotating sound sources in frequency-domain. Under normal circumstances, the configuration of microphone array is established in ring geometry centred around the rotating axis. Two interpolation methods for arbitrary microphone configurations are proposed by Jekosch and Sarradj (Acoustics, 2020). One is to construct a mesh between all stationary microphones using Delaunay-triangulation, another one is a meshless technique based on radial basis function. However, whether other spatial interpolation methods are available in VRA beamforming with arbitrary microphone configurations is still unclear. This paper adds several new spatial interpolation methods in VRA beamforming and detailedly compares the performances of these interpolation methods in simulations. The simulating results demonstrated that all these interpolation methods are successfully applied in VRA beamforming with arbitrary microphone configurations. Inverse distance weighting interpolation method owns the best performance in rotating sound source localization. In addition, all these interpolation methods have poor spectrum construction capability and sound source strength precision.

Microphone arrays are an essential tool in the field of bioacoustics as they provide a non-intrusive way to study animal vocalizations and monitor their movement and behavior. Microphone arrays can be used for passive localization and tracking of sound sources while analyzing beamforming or spatial filtering of the emitted sound. Studying free roaming animals usually requires setting up equipment over large areas and attaching a tracking device to the animal which may alter their behavior. However, monitoring vocalizing animals through arrays of microphones, spatially distributed over their habitat has the advantage that unrestricted/unmanipulated animals can be observed. Important insights have been achieved through the use of microphone arrays, such as the convergent acoustic field of view in echolocating bats or context-dependent functions of avian duets. Here we show the development and application of large flexible microphone arrays that can be used to localize and track any vocalizing animal and study their bio-acoustic behavior. In a first experiment with hunting pallid bats the acoustic data acquired from a dense array with 64 microphones revealed details of the bats’ echolocation beam in previously unseen resolution. We also demonstrate the flexibility of the proposed microphone array system in a second experiment, where we used a different array architecture allowing to simultaneously localize several species of vocalizing songbirds in a radius of 75 m. Our technology makes it possible to do longer measurement campaigns over larger areas studying changing habitats and providing new insights for habitat conservation. The flexible nature of the technology also makes it possible to create dense microphone arrays that can enhance our understanding in various fields of bioacoustics and can help to tackle the analytics of complex behaviors of vocalizing animals., Verreycken and colleagues present a methods paper detailing construction of large, scalable microphone arrays for bio-acoustic monitoring of animal species. They demonstrate the efficacy of their method in detailing bat hunting echolocation beams with a 64-microphone array, and localizing songbirds across a larger habitat.

Sensor array-based systems, which adopt time difference of arrival (TDOA) measurements among the sensors, have found many robotic applications. However, for existing frameworks and systems to be useful, the sensor array needs to be calibrated accurately. Of particular interest in this article are microphone array-based robot audition systems. In our recent work, by using a moving sound source, and the graph-based formulation of simultaneous localization and mapping (SLAM), we have proposed a framework for joint sound source localization and calibration of microphone array geometrical information, together with the estimation of microphone time offset and clock difference/drift rates. However, a thorough study on the identifiability question, termed observability analysis here, in the SLAM framework for microphone array calibration and sound source localization, is still lacking in the literature. In this article, we will fill the abovementioned gap via a Fisher information matrix approach. Motivated by the equivalence between the full column rankness of the Fisher information matrix and the Jacobian matrix, we leverage the structure of the latter associated with the SLAM formulation, and present necessary and sufficient conditions guaranteeing its full column rankness, which lead to parameter identifiability. We have thoroughly discussed the 3-D case with asynchronous (with both time offset and clock drifts, or with only one of them) and synchronous microphone array, respectively. These conditions are closely related to the motion varieties of the sound source and the microphone array configuration, and have intuitive and physical interpretations. Based on the established conditions, we have also discovered some particular cases where observability is impossible. Connections with calibration of other sensors will also be discussed, amongst others. To our best knowledge, this is the first systematic work on observability analysis of SLAM-based microphone array calibration and sound source localization. The tools and concepts used in this article are also applicable to other TDOA sensing modalities such as ultrawide band (UWB) sensors.

Voice interaction is friendly and convenient for users. Smart devices such as Amazon Echo allow users to interact with them by voice commands and become increasingly popular in our daily life. In recent years, research works focus on using the microphone array built in smart devices to localize the user's position, which adds additional context information to voice commands. In contrast, few works explore the user's head orientation, which also contains useful context information. For example, when a user says, "turn on the light", the head orientation could infer which light the user is referring to. Existing model-based works require a large number of microphone arrays to form an array network, while machine learning-based approaches need laborious data collection and training workload. The high deployment/usage cost of these methods is unfriendly to users. In this paper, we propose HOE, a model-based system that enables Head Orientation Estimation for smart devices with only two microphone arrays, which requires a lower training overhead than previous approaches. HOE first estimates the user's head orientation candidates by measuring the voice energy radiation pattern. Then, the voice frequency radiation pattern is leveraged to obtain the final result. Real-world experiments are conducted, and the results show that HOE can achieve a median estimation error of 23 degrees. To the best of our knowledge, HOE is the first model-based attempt to estimate the head orientation by only two microphone arrays without the arduous data training overhead.

In close-talking applications, such as mobile phones, selective measurement of the near-field sound is desirable as it can be used to improve the signal-to-noise ratio. In this study, the theory of generalized radiation modes is applied to the design of microphone arrays. The generalized radiation modes are formulated as a generalized eigenvalue problem related to the specific acoustic impedances on the array surface. The real eigenvalue corresponds to the near-to-far ratio for each mode and the real eigenvector indicates the modal shape, i.e., the amplitudes and phases of the individual microphones. The microphone array is designed according to the eigenvector with the largest eigenvalue so as to maximize the near-to-far ratio. The theory is verified based on computer simulations. The proposed method is also compared with conventional gradient microphones via numerical examples.

Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device's interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source's location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach.

When the background noise is large or the sound source is far away from the microphone array, the localization accuracy of target sound sources will decrease dramatically. In order to solve this problem, a localization method of multi-aperture acoustic array based on time difference of arrival(TDOA) is proposed. The multi-aperture acoustic array is constructed by two small sub microphone arrays, which is supposed to surpass the performance of the equivalent large array composed of all microphones. The positions of the target sound source in two local coordinates are separately estimated by the two sub microphone arrays based on the generalized trust-region sub-problem in the modified polar representation (GTRS-MPR) method. Through the space coordinate transformation, the estimated two positions of the target sound source in the two local coordinates can be transferred into one global coordinate. To optimize the final estimated position of the target sound source by the multi-aperture acoustic array, two spatial lines are formed between the reference microphones of the two sub-arrays and the estimated two sources’ positions in the global coordinate, and a search algorithm of shortest distance point based on the least-square method is proposed to fuse the localization results of the two sub-arrays. A simulation and an experimental platform are set up to verify the proposed method. The results show that the proposed method has higher localization accuracy compared with the existing methods. Especially, the accuracy of range estimation is greatly improved, which will be more suitable for the far-field sound source localization.

To understand the social interactions among songbirds, extracting the timing, position, and acoustic properties of their vocalizations is essential. We propose a framework for automatic and fine-scale extraction of spatial-spectral-temporal patterns of bird vocalizations in a densely populated environment. For this purpose, we used robot audition techniques to integrate information (i.e., the timing, direction of arrival, and separated sound of localized sources) from multiple microphone arrays (array of arrays) deployed in an environment, which is non-invasive. As a proof of concept of this framework, we examined the ability of the method to extract active vocalizations of multiple Zebra Finches in an outdoor mesh tent as a realistic situation in which they could fly and vocalize freely. We found that localization results of vocalizations reflected the arrangements of landmark spots in the environment such as nests or perches and some vocalizations were localized at non-landmark positions. We also classified their vocalizations as either songs or calls by using a simple method based on the tempo and length of the separated sounds, as an example of the use of the information obtained from the framework. Our proposed approach has great potential to understand their social interactions and the semantics or functions of their vocalizations considering the spatial relationships, although detailed understanding of the interaction would require analysis of more long-term recordings.

In this paper, microphone array technology was used to monitor the dynamic pulsed GMAW process. At first, the splash sound signal is successfully separated out based on FastICA blind signal separation algorithm, and its frequency domain energy distribution is mainly concentrated in the high frequency band of 6000−8000 Hz. Through time and frequency domain analysis, it is found that the short-time energy of 500−1000 Hz band of observed signal and the short-time energy of splash signal can identify the burn-through defects well, furthermore the ratio of them can be used as a robust feature because of its high sensitivity and anti-interference performance. Since the splash sound signal is a characteristic signal separated by microphone array, it is shown that the abundant dynamic information provided by microphone array can better assist in the identification and monitoring of welding defects compared to a single microphone. In order to solve the serious imbalance problem between positive and negative samples, the logistic regression and BP neural network model are improved based on the machine learning method. The experimental results show that the recognition accuracy of the optimization models have been greatly improved, even could reach 99.6 %.

Acoustic source localization techniques in combination with microphone array measurements have become an important tool for noise reduction tasks. A common technique for this purpose is acoustic beamforming, which can be used to determine the source locations and source distribution. Advantages are that common algorithms such as conventional beamforming, functional beamforming or deconvolution techniques (e.g., Clean-SC) are robust and fast. In most cases, however, a simple source model is applied and the Green’s function for free radiation is used as transfer function between source and microphone. Additionally, without any further signal processing, only stationary sound sources are covered. To overcome the limitation of stationary sound sources, two approaches of beamforming for rotating sound sources are presented, e.g., in an axial fan.Regarding the restrictions concerning source model and boundary conditions, an inverse method is proposed in which the wave equation in the frequency domain (Helmholtz equation) is solved with the corresponding boundary conditions using the finite element method. The inverse scheme is based on minimizing a Tikhonov functional matching measured microphone signals with simulated ones. This method identifies the amplitude and phase information of the acoustic sources so that the prevailing sound field can be with a high degree of accuracy.

An integrated version of the minimum variance distortionless response (MVDR) beamformer for speech enhancement using a microphone array has been recently developed, which merges the benefits of imposing constraints defined from both a relative transfer function (RTF) vector based on a priori knowledge and an RTF vector based on a data-dependent estimate. In this paper, the integrated MVDR beamformer is extended for use with a microphone configuration where a microphone array, local to a speech processing device, has access to the signals from multiple external microphones (XMs) randomly located in the acoustic environment. The integrated MVDR beamformer is reformulated as a quadratically constrained quadratic program (QCQP) with two constraints, one of which is related to the maximum tolerable speech distortion for the imposition of the a priori RTF vector and the other related to the maximum tolerable speech distortion for the imposition of the data-dependent RTF vector. An analysis of how these maximum tolerable speech distortions affect the behaviour of the QCQP is presented, followed by the discussion of a general tuning framework. The integrated MVDR beamformer is then evaluated with audio recordings from behind-the-ear hearing aid microphones and three XMs for a single desired speech source in a noisy environment. In comparison to relying solely on an a priori RTF vector or a data-dependent RTF vector, the results demonstrate that the integrated MVDR beamformer can be tuned to yield different enhanced speech signals, which may be more suitable for improving speech intelligibility despite changes in the desired speech source position and imperfectly estimated spatial correlation matrices.

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

Frequency response mismatches among microphones inevitably result in performance deterioration of signal processing algorithms for microphone arrays. A frequency response calibration method based on the consensus strategy is proposed in this article. Adaptive calibration filters concatenated with microphone channels are first introduced to compensate for the frequency response mismatches among microphones. Then, the frequency response mismatch problem is formulated as the minimization of a cost function measuring the spectrum mismatches among neighboring microphone channel outputs. Finally, the consensus strategy is used to design the calibration filter in each microphone channel. By using the consensus strategy, the proposed frequency response calibration method is robust to the room impulse response and noise differences among microphones. The proposed method outperforms gain calibration methods and the centralized frequency response calibration method. Experimental results reveal the validity of the proposed method.

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

An $M$ th-order linear differential microphone array (LDMA) is conventionally designed by using a linear array of $M+1$ closely-spaced microphones. It is known that the conventional LDMAs suffer from the white noise amplification problem which may cause significant performance degradation in the presence of sensor imperfections. In order to resolve this, a more advanced solution has been proposed to employ more than $M+1$ microphones in the design of the LDMAs, which is shown to be effective against the white noise amplification and, hence, the resultant LDMAs are known as the robust LDMAs. Previous studies have shown that sensor imperfections can lead to dramatic performance degradation or even failure of the LDMAs due to the presence of the mainlobe orientation reversal (MOR) phenomenon. Therefore, avoiding the occurrence of the detrimental MOR phenomenon can serve as a minimum condition for the design of robust LDMAs in the presence of sensor imperfections. However, it is not yet quite clear how the sensor imperfections, such as microphone gain and phase mismatches, affect the robust LDMAs. Particularly in practical design, it will be of interest to know what the requirement is on the microphone imperfection tolerance for a given number of microphones, or alternatively, how many microphones should be used with a given microphone imperfection tolerance to guarantee no occurrence of the MOR phenomenon, which remains to be addressed. Motivated by the above, in this paper we first give an in-depth analysis of the impact of microphone imperfections on the mainlobe orientation of the robust LDMAs, and reveal how the MOR phenomenon occurs with the robust LDMAs. Then based on our theoretical foundation, the microphone imperfection tolerance analysis is performed, which provides a useful guidance when incorporating the influence of sensor imperfections into the practical design of robust LDMAs. Extensive numerical results are also presented to validate the effectiveness of our analysis.

The problem of multiple acoustic source localization using observations from a microphone array network is investigated in this article. Multiple source signals are assumed to be window-disjoint-orthogonal (WDO) on the time-frequency (TF) domain and time delay of arrival (TDOA) measurements are extracted at each TF bin. A Bayesian network model is then proposed to jointly assign the measurements to different sources and estimate the acoustic source locations. Considering that the WDO assumption is usually violated under reverberant and noisy environments, we construct a relational network by coding the distance information between the distributed microphone arrays such that adjacent arrays have higher probabilities of observing the same acoustic source, which is able to mitigate the miss detection issues in adverse environments. A Laplace approximate variational inference method is introduced to estimate the hidden variables in the proposed Bayesian network model. Both simulations and real data experiments are performed. The results show that our proposed method is able to achieve better source localization accuracy than existing methods.

Recording multiple sound sources in a reverberant environment results in convolutive mixtures. Sound sources can be extracted from microphone array recordings of such mixtures using acoustic source separation techniques. Acoustic source separation using recordings obtained from rigid spherical microphone arrays (RSMA) benefit from the representation of sound fields as series of spherical harmonics. More specifically, RSMAs afford increased flexibility in acoustic beamforming and spatial filtering. We propose a data-driven DOA estimation and acoustic source separation method based on a dictionary-based sparse decomposition of sound fields. The proposed method involves identifying the time-frequency bins with contributions from a single source only and those with sensor noise or diffuse sound field components. The former set of bins is used in DOA estimation and beamforming in the sparse decomposition domain. The latter set is used to calculate the diffuse field covariance matrix used in Wiener post-filtering to improve the source separation performance further. We demonstrate the utility of the proposed method via extensive objective and subjective evaluations.

Microphone arrays combined with beamforming have been widely used to solve many important acoustic problems in a wide range of applications. Much effort has been devoted in the literature to microphone array beamforming, among which the Kronecker product beamforming method developed recently has demonstrated some interesting properties. Generally, this method decomposes the global beamforming filter into a Kronecker product of a number of sub-beamforming filters, each of which corresponds to a virtual subarray and can be designed individually. This decomposition not only reduces significantly the number of beamforming coefficients, but also can be explored to improve the robustness and flexibility of beamforming. This paper extends Kronecker product beamforming from two-dimensional arrays into three-dimensional cube arrays. We consider two decompositions, i.e., fully and partially separable ones. The former decomposes the entire array into three linear subarrays while the latter decomposes the entire array into a linear subarray and a planar one. Then, for each case, we derive the Kronecker product maximum white noise gain beamformer, the Kronecker product approximate maximum directivity factor (DF) beamformer, the Kronecker product null-steering beamformer, and the Kronecker product iterative maximum DF beamformer. Simulation results demonstrate the properties and advantages of the proposed beamformers.

Accurate device motion tracking enables many applications like Virtual Reality (VR) and Augmented Reality (AR). To make these applications available in people's daily life, low-cost acoustic-based motion tracking methods are proposed. However, existing acoustic-based methods are all based on distance estimation. These methods measure the distance between a speaker and a microphone. With a speaker or microphone array, it can get multiple estimated distances and further achieve multidimensional motion tracking. The weakness of distance-based motion tracking methods is that they need large array size to get accurate results. Some systems even require an array larger than 1 m. This weakness limits the adoption of existing solutions in a single device like a smart speaker. To solve this problem, we propose Acoustic Strength-based Angle Tracking (ASAT) System and further implement a motion tracking system based on ASAT. ASAT achieves angle tracking by creating a periodically changing sound field. A device with a microphone will sense the periodically changing sound strength in the sound field. When the device moves, the period of received sound strength will change. Thus we can derive the angle change and achieve angle tracking. The ASAT-based system can obtain the localization accuracy as 5 cm when the distance between the speaker and the microphone is in the range of 3 m.

Wideband acoustic imaging, which combines compressed sensing (CS) and microphone arrays, is widely used for locating acoustic sources. However, the location results of this method are unstable, and the computational efficiency is low. In this work, in order to improve the robustness and reduce the computational cost, a DCS-SOMP-SVD compressed sensing method, which combines the distributed compressed sensing using simultaneously orthogonal matching pursuit (DCS-SOMP) and singular value decomposition (SVD) is proposed. The performance of the DCS-SOMP-SVD is studied through both simulation and experiment. In the simulation, the locating results of the DCS-SOMP-SVD method are compared with the wideband BP method and the DCS-SOMP method. In terms of computational efficiency, the proposed method is as efficient as the DCS-SOMP method and more efficient than the wideband BP method. In terms of locating accuracy, the proposed method can still locate all sources when the signal to noise ratio (SNR) is − 20 dB, while the wideband BP method and the DCS-SOMP method can only locate all sources when the SNR is higher than 0 dB. The performance of the proposed method can be improved by expanding the frequency range. Moreover, there is no extra source in the maps of the proposed method, even though the target sparsity is overestimated. Finally, a gas leak experiment is conducted to verify the feasibility of the DCS-SOMP-SVD method in the practical engineering environment. The experimental results show that the proposed method can locate both two leak sources in different frequency ranges. This research proposes a DCS-SOMP-SVD method which has sufficient robustness and low computational cost for wideband acoustic imaging.

Spherical microphone arrays (SMAs) are widely used to capture spatial sound fields that can then be rendered in various ways as a virtual acoustic environment (VAE) including headphone-based binaural synthesis. Several practical limitations have a significant impact on the fidelity of the rendered VAE. The finite number of microphones of SMAs leads to spatial undersampling of the captured sound field, which, on the one hand, induces spatial aliasing artifacts and, on the other hand, limits the order of the spherical harmonics (SH) representation. Several approaches have been presented in the literature that aim to mitigate the perceptual impairments due to these limitations. In this article, we present a listening experiment evaluating the perceptual improvements of binaural rendering of undersampled SMA data that can be achieved using state-of-the-art mitigation approaches. In particular, we examined the Magnitude Least-Squares algorithm, the Bandwidth Extraction Algorithm for Microphone Arrays, Spherical Head Filters, SH Tapering, and a newly proposed equalization filter. In the experiment, subjects rated the perceived differences between a dummy head and the corresponding SMA auralization. We found that most mitigation approaches lead to significant perceptual improvements, even though audible differences to the reference remain.

Subject of Research. The paper considers the main quantitative characteristics of microphone arrays in their performance analysis and beamforming algorithms applied for their study. Methods. The principal characteristics, calculation approaches and evaluations were examined. The most widespread beamforming algorithms were analyzed, such as: Delay-and-Sum (DAS), minimum variance distortionless response (MVDR), Frost’s algorithm or linearly constrained minimum variance (LCMV), generalized sidelobe canceller (GSC). The calculations and comparative analysis of the algorithms were performed in Matlab simulation environment. The following quantitative characteristics were obtained: signal-to-noise ratio, signal-interference-noise ratio, directivity index, directivity characteristics, and beamwidth. Main Results. Algorithm comparison results are presented on the example of a linear microphone array. The linearly constrained minimum variance algorithm has shown the most satisfactory results. The simulation results must be clarified by real experiments. Practical Relevance. The results of the work can be used for analysis of more complex microphone arrays and acoustic structures, such as microphone arrays with “floating” geometry and distributed microphone arrays.

With the popularity of intelligent audio systems in recent years, their vulnerabilities have become an increasing public concern. Existing studies have designed a set of machine-induced audio attacks, such as replay attacks, synthesis attacks, hidden voice commands, inaudible attacks, and audio adversarial examples, which could expose users to serious security and privacy threats. To defend against these attacks, existing efforts have been treating them individually. While they have yielded reasonably good performance in certain cases, they can hardly be combined into an all-in-one solution to be deployed on the audio systems in practice. Additionally, modern intelligent audio devices, such as Amazon Echo and Apple HomePod, usually come equipped with microphone arrays for far-field voice recognition and noise reduction. Existing defense strategies have been focusing on single- and dual-channel audio, while only few studies have explored using multi-channel microphone array for defending specific types of audio attack. Motivated by the lack of systematic research on defending miscellaneous audio attacks and the potential benefits of multi-channel audio, this paper builds a holistic solution for detecting machine-induced audio attacks leveraging multi-channel microphone arrays on modern intelligent audio systems. Specifically, we utilize magnitude and phase spectrograms of multi-channel audio to extract spatial information and leverage a deep learning model to detect the fundamental difference between human speech and adversarial audio generated by the playback machines. Moreover, we adopt an unsupervised domain adaptation training framework to further improve the model's generalizability in new acoustic environments. Evaluation is conducted under various settings on a public multi-channel replay attack dataset and a self-collected multi-channel audio attack dataset involving 5 types of advanced audio attacks. The results show that our method can achieve an equal error rate (EER) as low as 6.6% in detecting a variety of machine-induced attacks. Even in new acoustic environments, our method can still achieve an EER as low as 8.8%.

An advanced duct microphone array analysis based on a user friendly iterative Bayesian Inverse Approach (iBIA) has been successfully applied to assess the modal content and the Sound Power Level of fan/outlet guide vanes (OGV) broadband noise. It is shown that iBIA provides considerable reduction of artefacts associated to array sidelobes leading to an improved dynamic range in the reconstructed mode distribution plots. The method also benefits from a data-dependent and fully automated regularisation procedure. Moreover, it allows to control the sparsity degree of the reconstructed modal content: thus different sparsity levels can be tailored for specific noise components, such as fan tones or broadband mode components. It also applies directly to the cross-spectral matrix of measurements, thus advantage can be taken of averaging the data over several snapshots whenever the assumption of stationarity and ergodicity holds. These features facilitate its application to industrially relevant configurations such as the UFFA fan test rig that was operated by AneCom AeroTest in order to deliver reference data for broadband noise modelling in the frame of the European Project TurboNoiseBB. The iBIA has been applied to perform a full azimuthal and radial mode detection as well as axial-wavenumber and azimuthal decompositions. Two configurations of fan/OGV-spacing along two working lines have been tested. The radiated noise has been measured by in-duct microphone arrays located at the intake and downstream of the fan module. Longer rotor-stator gaps are shown to reduce high frequency levels at approach conditions for both inlet and exhaust noise over a large frequency range. The benefit of long gaps tends to vanish for higher fan speeds and even leads to noise increase at high frequencies. Duct sound power estimates obtained from a full azimuthal and radial decomposition have been compared to estimates from both the wavenumber and the azimuthal decompositions, the latter two requiring further assumptions regarding the modal energy distribution. Based on this approach, the widely used equal energy per mode and equal energy density per mode assumptions have thus been evaluated on a large industrially relevant experimental data set.

A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar microphone array. In our approach, we use the phase component of the short-time Fourier transform (STFT) of the microphone array’s signals as the input feature for the CNN and a DoA probability density map as the training target. Our findings imply that our method outperforms the currently available methods for multiple sound source DoA estimation in both accuracy and speed.

This paper proposes a method for estimating the angle-dependent sound absorption coefficient of a large material sample using a compact microphone array. The method relies on the description of the pressure field as a pair of in-going and out-going waves or using an image source model and stands as a generalization of the classical two-microphone method. The array includes an irregular spacing normal to the surface to avoid spatial aliasing. Furthermore, the benefit of additional microphones parallel to the sample is investigated, while keeping the array compact. The approach is validated against the transfer matrix method as well as against locally and non-locally reactive surface models and compared to the two-microphone method. The sensitivity of the estimation to uncertainties in the microphone positions is evaluated by means of a Monte Carlo approach. Measurements above melamine foam and gravel samples are presented and illustrate the reduced uncertainty in the sound absorption estimation. In particular, the proposed method exhibits improved robustness compared to the two-microphone method, especially at low frequencies. ispartof: Journal Of The Acoustical Society Of America vol:150 issue:4 pages:2388-2400 ispartof: location:United States status: Published online

This paper addresses the problem of microphone array generalization for deep-learning-based end-to-end multichannel speech enhancement. We aim to train a unique deep neural network (DNN) potentially performing well on unseen microphone arrays. The microphone array geometry shapes the network's parameters when training on a fixed microphone array, and thus restricts the generalization of the trained network to another microphone array. To resolve this problem, a single network is trained using data recorded by various microphone arrays of different geometries. We design three variants of our recently proposed narrowband network to cope with the agnostic number of microphones. Overall, the goal is to make the network learn the universal information for speech enhancement that is available for any array geometry, rather than learn the one-array-dedicated characteristics. The experiments on both simulated and real room impulse responses (RIR) demonstrate the excellent across-array generalization capability of the proposed networks, in the sense that their performance measures are very close to, or even exceed the network trained with test arrays. Moreover, they notably outperform various beamforming methods and other advanced deep-learning-based methods., Comment: Submitted to Interspeech Conference 2021

Sound source localization is becoming a fundamental ability for robots, which makes use of audio clues. However, few studies have considered how to leverage the motion of the robot to locate the sound source more precisely, especially when the robot’s acoustic sensors are partially impaired. In this paper, we propose a new method for the mobile robot to better approach the sound object based on the idea of sensorimotor coordination. Firstly, the optimal direction near fine microphone sensors will be found to defeat the influence of some bad acoustic sensors. Secondly, the direction of the sound object is estimated with the HOA encoding technique using a spherical microphone array. Thirdly, the robot actively rotates to refine the position of the sound source combining the estimated direction and odometry information in a coordinated way considering sensors partially damaged. And the robot is approaching the target while the position of the sound object is calculated iteratively. Experimental results in the simulated and real-world environment show the robot can approach the sound object more accurately using the proposed method.

The generalized sidelobe cancellation (GSC) algorithm is widely used in microphone array speech enhancement. However, there are some problems related to it, such as the direction of arrive (DOA) estimation errors, insufficient spatial information utilization, and delay estimation errors, which make the blocking matrix (BM) unable to completely suppress the expected speech, resulting in leakage of the expected speech to the adaptive noise cancellation (ANC) module and the distortion of the desired voice. In this paper, we design a lightweight dual-channel RNN network structure to train the voice activity detection (VAD) which is a key module in GSC. The training process not only uses the amplitude characteristics of the two channels, but also adds the phase difference characteristics. The addition of spatial information can effectively improve the discrimination accuracy of VAD. Then we combine that VAD with the adaptive blocking matrix (ABM) and adaptive noise cancellation (ANC) for noise reduction processing. The simulation results show that the proposed method can significantly reduce the leakage of the desired voice while improving the noise reduction capability of GSC.

This paper presents the results of a study evaluating the human perception of the noise produced by four different small quadcopter unmanned aerial vehicles (UAVs). This study utilised measurements and recordings of the noise produced by the quadcopter UAVs in hover and in constant-speed flight at a fixed altitude. Measurements made using a ½″ microphone were used to calculate a range of different noise metrics for each noise event. Noise recordings were also made using a spherical microphone array (an Eigenmike system). The recordings were reproduced using a 3D sound reproduction system installed in a large anechoic chamber located at The University of Auckland. Thirty-seven participants were subjected to the recordings and asked to rate their levels of annoyance in response to the noise, and asked to perform a simple cognitive task in order to assess the level of distraction caused by the noise. This study discusses the noise levels measured during the test and how the various noise metrics relate to the annoyance ratings. It was found that annoyance strongly correlates with the sound pressure level and loudness metrics, and that there is a very strong correlation between the annoyance caused by a UAV in hover and in flyby at the same height. While some significant differences between the distraction caused by the UAV noise for different cases were observed in the cognitive distraction test, the results were inconclusive. This was likely due to a ceiling effect observed in the participants’ test scores.

This article presents a theoretical study of differential beamforming with uniform linear arrays. By defining a forward spatial difference operator, any order of the spatial difference of the observed signals can be represented as a product of a difference operator matrix and the microphone array observations. Consequently, differential beamforming is implemented in two stages, where the first one obtains spatial difference of the observations and the second stage optimizes the beamformer. The major contributions of this article are as follows. First, we propose a new theory of differential beamforming with uniform linear arrays, which shows clearly the connection between the conventional differential beamforming and the null-constrained differential beamforming methods. This provides some new insight into the design of differential beamformers. Second, we deduce some new differential beamformers, where conventional beamforming may be seen as a particular case. Specifically, we derive the maximum white noise gain (MWNG), maximum directivity factor (MDF), parameterized MDF, and parameterized maximum front-to-back ratio differential beamformers. Third, we further extend the idea of how to design optimal differential beamformers by combining both the observed signals and their spatial differences.

Beamforming is a powerful technique to achieve acoustic imaging in far-field. However, its spatial resolution is strongly blurred by the point spread function (PSF) of phased microphone array. Due to the limitation of array aperture and microphone density, the PSF is far from Dirac delta function, so that it is difficult to obtain a high-resolution beamforming image at low-frequencies (e.g.500-1500Hz). This paper proposes a Bayesian inference method based on Non-synchronous Array Measurements (Bi-NAM) so as to refine the PSF and break through the beamforming limitation for low-frequency source imaging. Firstly, by sequentially moving prototype array at different positions, the non-synchronous measurements can get a sizeable synthetic aperture and high density of microphones. The synthetic cross-spectrum matrix (CSM) can significantly improve the beamforming performance. To confine the approximation error of synthetic CSM and the uncertainty of forward model, as well as the noise interference, a Bayesian inference based on joint maximum a posterior (JMAP) is proposed to solve an ill-posed inverse problem. A Student-t prior is employed to enforce the sparse property of acoustic strength distribution. The background noise can be adaptively modeled by the Student-t distribution, which is related to some of the typical symmetric distributions. Then the hyper-parameters in JMAP inference are efficiently estimated by the Bayesian hierarchical framework. Through experimental data, the proposed Bi-NAM approach is confirmed to achieve high-resolution acoustic imaging at 1000Hz and 800Hz, respectively, even under the Laplace noise interference.

A comparative analysis of the dependence of the directivity index on frequency for a reflex microphone, an organ type microphone and a microphone array with their limited dimensions, as well as the dependence of spatial selectivity on the aperture at fixed frequencies, is carried out. A design scheme is proposed for a small-sized acoustic reconnaissance device based on a microphone array using microelectromechanical microphone systems technology, with a hypercardioid type of radiation pattern, and an over-directivity algorithm, which is a combination of a differential algorithm and a delay and sum algorithm. The optimal step between the microphones in the array was calculated at the set maximum working frequency of 10 kHz, the number of single microphones was calculated for the overall dimensions of the acoustic reconnaissance equipment of 30 cm and 10 cm. The use of the selected type of narrow-directional microphone with the proposed algorithm for digital signal processing leads to an increase in the directivity index of the microphone array to the level of the reflex microphone, which can significantly reduce the size (from 30 cm to 10 cm) and the number of microphones (from 12 to 4) design while maintaining spatial selectivity.

This paper describes the design of an unmanned aerial vehicle (UAV) mounted system for recording sound from a targeted source or direction with a minimum amount of noise. The objective of the study is: i) select an optimum electric motor-propeller combination which minimises the level of unwanted noise from these components at the microphone positions; ii) design a microphone array configuration suitable for installation on a UAV; and iii) develop a signal processing algorithm that significantly reduces noise in the recording. The designed system was prototyped and tested in various scenarios simulated in an anechoic chamber as well as by recordings collected from a system mounted on a hovering UAV. The system succeeded in reducing the propeller noise level up to approximately 12 dB compared to the recording by a shotgun microphone. Results of subjective listening tests suggest the quality of the recording made by the designed UAV mounted system is significantly better than that of the recording by the shotgun microphone.

Nonlinear acoustic echo cancellation AEC is a highly challenging task in a single-microphone; hence, the AEC technique with a microphone array has also been considered to more effectively reduce the residual echo. However, these algorithms track only a linear acoustic path between the loudspeaker and the microphone array. This study proposes a microphone array form of the single-microphone nonlinear AEC NAEC algorithm in the reverberant condition. We extend a single-microphone-based model of the nonlinear acoustic echo to the microphone array case and propose the modeling of the acoustic transfer function ATF vector extended with a power series using a state-space equation. The Kalman filter is also adapted to optimally and recursively estimate the ATF vector. Furthermore, low-rank approximation and multi-microphone Wiener filtering are applied to estimate the multi-microphone near-end speech covariance, which results in the microphone array NAEC algorithm showing a consistently outstanding performance under severe signal-to-echo ratio SER and highly reverberant conditions. Consequently, our approach outperforms conventional methods regarding echo reduction and near-end speech quality for a wide range of SER and reverberation conditions.

Many workers are exposed daily to excessive noise levels. In order to reduce the noise exposure at the workstation, the main noise sources have to be detected. This task can be done with a microphone array and a source localization technique. Previous studies have shown promising results with time domain beamforming based on the generalized cross-correlation technique. The objective of this work is to propose an optimal spherical microphone array geometry dedicated to this technique. A cost function based on the symmetry of the aperture angle maps is proposed and is maximized using a Nonlinear Optimization by Mesh Adaptive Direct Search. Numerical results show that the optimized geometry improves the noise source map by reducing the side lobe amplitude without increasing the main lobe surface. Experimental measurements are carried out in a semi-anechoic chamber with prototyped spherical microphone arrays confirming that the optimized microphone array improves the quality of the noise source map.

The classical algorithm for localizing noise sources using a planar microphone array is generalized to the case of its application to dipole-type sources. The developed algorithm was verified and validated via data processing of experimental studies of cylinder flow noise and jet–plate interaction noise. We compare the results of localizing dipole noise sources using a planar microphone array employing the developed algorithm and an azimuthal array based on the azimuthal decomposition technique.

While substantial noise reduction and speech enhancement can be achieved with multiple microphones organized in an array, in some cases, such as when the microphone spacings are quite close, it can also be quite limited. This degradation can, however, be resolved by the introduction of one or more external microphones ( $\text{XM}$ s) into the same physical space as the local microphone array ( $\text{LMA}$ ). In this paper, three methods of extending an $\text{LMA}$ -based generalized sidelobe canceller ( $\text{GSC-LMA}$ ) with multiple $\text{XM}$ s are proposed in such a manner that the relative transfer function pertaining to the $\text{LMA}$ is treated as a priori knowledge. Two of these methods involve a procedure for completing an extended blocking matrix, whereas the third uses the speech estimate from the $\text{GSC-LMA}$ directly with an orthogonalized version of the $\text{XM}$ signals to obtain an improved speech estimate via a rank-1 generalized eigenvalue decomposition. All three methods were evaluated with recorded data from an office room and it was found that the third method could offer the most improvement. It was also shown that in using this method, the speech estimate from the $\text{GSC-LMA}$ was not compromised and would be available to the listener if so desired, along with the improved speech estimate that uses both the $\text{LMA}$ and $\text{XM}$ s.

In this paper, we proposed an optimisation method to solve the problem of sound source localisation and calibration of an asynchronous microphone array. This method is based on the graph-based formulation of the simultaneous localisation and mapping problem. In this formulation, a moving sound source is considered to be observed from a static microphone array. Traditional approaches for sound source localisation rely on the well-known geometrical information of the array and synchronous readings of the audio signals. Recent work relaxed these two requirements by estimating the temporal offset between pair of microphones based on the assumption that the clock timing of each microphone is exactly the same. This assumption requires the sound cards to be identically manufactured, which in practice is not possible to achieve. Hereby an approach is proposed to jointly estimate the array geometrical information, time offset and clock difference/drift rate of each microphone together with the location of a moving sound source. In addition, an observability analysis of the system is performed to investigate the most suitable configuration for sound source localisation. Simulation and experimental results are presented, which prove the effectiveness of the proposed methodology.

In this paper, we propose a multichannel active noise control (ANC) system combined with noise source separation by microphone arrays. A multichannel feedforward ANC system uses multiple reference microphones located close to noise sources and can reduce various acoustic noises such as broadband noise. In general, it is difficult to place the reference microphones close to the noise sources owing to physical constraints. In this case, the reference signals, which are the outputs of reference microphones, are highly correlated with each other, the noise reduction performance of the multichannel ANC system is degraded. In other words, the reference microphones detect the highly correlated noises, reducing the noise reduction ability of the ANC system. To solve this problem, a multichannel feedforward ANC system with a noise source separation technique is proposed. This system uses microphone arrays located at the reference microphone positions. The mixed signal is separated from the target noise and other components by noise source separation and the separated signals are used to update the noise control filters. Simulation results demonstrate that the proposed ANC system can separate the target noise and the disturbance noise and effectively reduce the target noise.

We propose a novel speaker-dependent speech separation framework for the challenging CHiME-5 acoustic environments, exploiting advantages of both deep learning based and conventional preprocessing techniques to prepare data effectively for separating target speech from multi-talker mixed speech collected with multiple microphone arrays. First, a series of multi-channel operations is conducted to reduce existing reverberation and noise, and a single-channel deep learning based speech enhancement model is used to predict speech presence probabilities. Next, a two-stage supervised speech separation approach, using oracle speaker diarization information from CHiME-5, is proposed to separate speech of a target speaker from interference speakers in mixed speech. Given a set of three estimated masks of the background noise, the target speaker and the interference speakers from single-channel speech enhancement and separation models, a complex Gaussian mixture model based generalized eigenvalue beamformer is then used for enhancing the signal at the reference array while avoiding the speaker permutation issue. Furthermore, the proposed front-end can generate a large variety of processed data for an ensemble of speech recognition results. Experiments on the development set have shown that the proposed two-stage approach can yield significant improvements of recognition performance over the official baseline system and achieved top accuracies in all four competing evaluation categories among all systems submitted to the CHiME-5 Challenge.

This study examines the estimation of the surface impedance of an absorber with microphone arrays. Two array geometries are compared—a rigid spherical array and a double layer planar array. The impedance is estimated via reconstructing the sound field (pressure and particle velocity) on the absorber's surface, using a plane wave expansion. The comparison is carried out by studying the numerical properties of the two arrays as well as through experimental tests.This study examines the estimation of the surface impedance of an absorber with microphone arrays. Two array geometries are compared—a rigid spherical array and a double layer planar array. The impedance is estimated via reconstructing the sound field (pressure and particle velocity) on the absorber's surface, using a plane wave expansion. The comparison is carried out by studying the numerical properties of the two arrays as well as through experimental tests.

In this paper, a robust indoor localization method using microphone pairs and asynchronous acoustic beacons was proposed. The proposed method is applicable even with a two-channel microphone pair, which is the minimal configuration of a microphone array. The proposed method estimates location by using the cross-correlation functions of the measured signals as location likelihoods. Three experiments were conducted to evaluate the proposed method. Four beacons were located at the corners of a localizing area of 4 m by 4 m and emitted signals with a bandwidth of 2 kHz. The localization results were compared to the previous method with deterministic direction-of-arrival estimation. The 90th percentiles of the localization error were 0.23 m for the proposed method with two microphones, 0.19 m for the proposed method with four microphones, and 0.30 m for the previous method under conditions without significant reverberation. Under a condition with reflective walls, the 90th percentile of the localization error of the previous method increased to 0.49 m, while that of the proposed method was only increased to 0.23 m for two microphones and 0.19 m for four microphones. The proposed method contributes to a robust localization in indoor environments and relieves the constraints of receiver configuration.

Ducted propellers are an interesting design choice for unmanned aerial vehicle (UAV) concepts due to a potential increase of the propeller efficiency. In such designs, it is commonly assumed that introducing the duct also results in an overall noise reduction. The objective of this work is to experimentally analyze and quantify noise of a ducted propeller suitable to be installed on a medium size UAV (wingspan 5–10 m). A microphone array is used for recording the noise levels at each microphone position and used collectively to localize noise sources with beamforming. Different types of noise sources are considered (an omni-directional source and a propeller). In addition, the effect of the presence of an incoming airflow is assessed. With no incoming airflow, it is found that the duct significantly modifies the noise radiation both in the frequency and the spatial domain. With an incoming airflow, the effect of the duct on the frequency content of the signal is almost eliminated. The fact that for this case the harmonics become lower results in a reduction of the received noise levels. Also the directivity changes. These insights are of importance in efforts towards modeling the effects of ducts for complex noise sources such as propellers.

To phased microphone array for sound source localization, algorithm with both high computational efficiency and high precision is a persistent pursuit. In this paper convolutional neural network (CNN) a kind of deep learning is preliminarily applied as a new algorithm. At high frequency CNN can reconstruct the sound localizations with excellent spatial resolution as good as DAMAS, within a very short time as short as conventional beamforming. This exciting result means that CNN perfectly finds source distribution directly from cross-spectral matrix without given propagation function in advance, and thus CNN deserves to be further explored as a new algorithm., Comment: 7 pages, 3 figures

In this paper, we investigate robust design methods for broadband beamformers in reverberant environments. In the design formulation room reverberation as well as robustness to amplitude and phase mismatches in the microphones have been included. Particularly, the direct path and the reflections are separated in the design such that there is a penalty on the reflective part. This approach is different from the commonly studied problem of dereverberation of a single point source as the investigated design is made over a region in space. A single point derverberation is not a very practical approach due to a high sensitivity to position changes. Thus in order to obtain more practical microphone array designs, we study methods that optimize performance over areas in space. The design problem has been formulated in four different ways; (i) using direct path only representing a traditional beamformer design method, (ii) using a robust design method which considers robustness against the microphone characteristics (gain and phase) by optimizing the mean performance, (iii) by including room impulse response in the design and finally, (iv) using both robustness and room impulse response in the design. Simulation results show that robust direct path based beamformer can achieve approximately the same performance as including room response in the design in many reverberation environments. The proposed method provides robustness over larger variations in the reverberation environment. This means that the robust direct path based method which is based on mean variations in gain and phase can be used in low- to medium ($$T_{60}$$ = 100–300 ms) reverberant environments with good result.

Three dimensional acoustic soundfields can be represented by a set of spherical harmonic coefficients that are extracted from pressure signals recorded by a spherical microphone array. The extraction method used and truncation order chosen introduce errors of spatial aliasing in the coefficients and truncation error in a reconstructed signal. A spatial Wiener filter (SWF) extraction method is proposed in this paper, using second order statistics of typical soundfield characteristics (signal power, estimated source locations, and internal microphone noise) and accounts for the presence of coefficients beyond the truncation order to reduce spatial aliasing in the extracted coefficients. The SWF can also distinguish between "wanted" and "unwanted" sources, reducing the contributions of unwanted sources to the extracted coefficients. The SWF is compared against the state of the art methods; regularized inverse (or generalized inverse), and orthonormal extraction methods, which are explored under a similar framework to the SWF. The authors compare these methods and show the benefit of the SWF for plane waves, with varying assumptions about the source characteristics. The SWF can also extract coefficients beyond the traditional truncation limit of a given array, unlike other methods.

A listening experiment is presented in which subjects rated the perceived differences in terms of spaciousness and timbre between a headphone-based headtracked dummy head auralization of a sound source in different rooms and a headphone-based headtracked auralization of a spherical microphone array recording of the same scenario. The underlying auralizations were based on measured impulse responses to assure equal conditions. Rigid-sphere arrays with different amounts of microphones ranging from 50 to up to 1202 were emulated through sequential measurements, and spherical harmonics orders of up to 12 were tested. The results show that the array auralizations are partially indistinguishable from the direct dummy head auralization at a spherical harmonics order of 8 or higher if the virtual sound source is located at a lateral position. No significant reduction of the perceived differences with increasing order is observed for frontal virtual sound sources. In this case, small differences with respect to both spaciousness and timbre persist. The evaluation of lowpass-filtered stimuli shows that the perceived differences occur exclusively at higher frequencies and can therefore be attributed to spatial aliasing. The room had only a minor effect on the results.

This paper addresses the problem of 2D sound source localization using multiple microphone arrays in an outdoor environment. Two main issues exist in such localization. Since the localization perfo...