Virtual Microphone Research Articles

An online modeling virtual sensing technique based on kriging interpolation for active noise control

Combined with virtual sensing techniques, active noise control can generate quiet zones in places without error microphones. Many existing virtual sensing techniques require pretraining of the system with physical microphones placed temporarily at the control location as well as retraining if the primary sound field or the secondary paths change. This process responds slowly and is infeasible in many application scenarios. In this paper, we propose a virtual sensing technique based on kriging interpolation, which gives an unbiased sound pressure estimator with minimum variance, assuming the spatial correlation model. The proposed method only depends on the distances between physical and virtual microphones and does not depend on the prior information of the primary noise field, implying that the method naturally tracks the variations of the noise sources. Further, when the secondary paths change due to variations in the secondary sources or the position of the virtual microphones, we combine the virtual sensing technique with a secondary path online modeling method, which enables the system to track the time-varying secondary paths and thus ensure the convergence of the active noise control system.

The Influence of Diffuse-Field Equalization on the Perception of Spatial Aliasing Introduced by Spherical Microphone Arrays

When a sound field is sampled by a finite number of microphones, the upper-frequency range of the recorded content is affected by spatial aliasing. The frequency beyond which aliasing-induced artifacts occur is related to the distance between a microphone and its nearest neighbors. In this study, ambisonics signals of order N = 7 were encoded from simulated recordings made by virtual spherical microphone arrays of varying characteristics (radius, sampling scheme, and sampling grid order). A diffuse-field equalization, the role of which is to lessen the aliasing-induced spectral coloration, was also considered in the tested conditions. These aliased stimuli were then compared with unaliased stimuli of order N = 7. Results showed that the diffuse-field equalization had a great impact on the perceived differences. A perceptual threshold was calculated in order to determine whether aliasing artifacts are likely to be audible based on the amount of aliasing-induced encoding error.

Virtual feedback control of interior road noise based on headrest loudspeakers

This paper analyzes the factors that affect the noise reduction performance of feedback control based on remote microphone technique (RMT) and how these factors can be exploited to improve the control performance. First, simulations were conducted to compare the noise reduction performance of employing feedback control directly at the virtual microphone (located at the ear position) and on the physical microphone (located on the headrest), as well as the RMT-based feedback control system. Then the impact of the delay of the virtual secondary path and the coherence between physical and virtual signals on the noise reduction performance of the RMT-based feedback control system was analyzed. It is found that the noise reduction performance can be improved by reducing the delay of the virtual secondary path or increasing the number of physical microphones. Finally, experiments of a dual-channel control system conducted inside an electric car cabin demonstrate that the feedback control strategy based on RMT achieves a binaural noise reduction of 3.4 dBA when employing 4 physical microphones to estimate sound pressure at the ear positions. This approach achieves similar performance but is more cost-effective than a feedforward system using multiple reference sensors.

An Experimental Performance Assessment of Temporal Convolutional Networks for Microphone Virtualization in a Car Cabin.

In this paper, the experimental results on microphone virtualization in realistic automotive scenarios are presented. A Temporal Convolutional Network (TCN) was designed in order to estimate the acoustic signal at the driver's ear positions based on the knowledge of monitoring microphone signals at different positions-a technique known as virtual microphone. An experimental setup was implemented on a popular B-segment car to acquire the acoustic field within the cabin while running on smooth asphalt at variable speeds. In order to test the potentiality of the TCN, microphone signals were recorded in two different scenarios, either with or without the front passenger. Our experimental results show that, when training is performed in both scenarios, the adopted TCN is able to robustly adapt to different conditions and guarantee a good average performance. Furthermore, an investigation on the parameters of the Neural Network (NN) that guarantee the sufficient accuracy of the estimation of the virtual microphone signals while maintaining a low computational complexity is presented.

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.

Reduced-order equivalent source modeling of aeroacoustic sources

The acoustic far-field prediction of high-fidelity aerodynamic simulation data is still not accurately possible when accounting for the surface reflections from an aircraft’s fuselage or wings. By presenting a general method for order-reduced equivalent source modeling of aeroacoustic sound sources, this work is bridging the gap between high-fidelity aerodynamic simulations and its integration to auralization frameworks. At the example of a numerically simulated jet, this study shows the computation of an equivalent source model with minimal complexity by means of spherical harmonic (SH) coefficients. The minimal source extension, in which all acoustic sources of a flow field are confined, is computed by using spherical Hankel extrapolation of sound pressure data from virtual concentric microphone arrays. The result of the SH transform shows that the dominant energy contribution can be associated to nine elementary sound sources. The resulting equivalent source model of jet noise provides a convenient date interface between large-scale computational fluid dynamics and acoustics simulations.

An analysis of a feedback active noise control system using the remote microphone technique

This paper examines strategies in virtual sensing feedback active noise control systems and compares the noise reduction performance of three strategies: using physical microphones for direct feedback control, using virtual microphones for direct feedback control, and using physical microphones for indirect virtual feedback control. For a primary sound field with incident waves in a single direction, simulation results indicate that employing a physical microphone for indirect virtual feedback control consistently yields superior noise reduction performance than that using a physical microphone for direct feedback control. However, if the causal requirement between physical and virtual microphones is not satisfied, the increase in the equivalent secondary path delay deteriorates noise reduction performance while utilizing physical microphones for indirect virtual feedback control. For a primary sound field containing waves from multiple directions, the simulation results show that the noise reduction performance is affected by both the coherence and causality between the physical and virtual microphone signals.

Robustness optimization of active headrest with virtual sensing to head rotation

The movement and rotation of the human head will cause changes of acoustic transfer functions, leading to the performance deterioration of active headrests with virtual microphones. This paper uses a transfer response model optimization method to improve the performance robustness of active headrests with remote microphones to the head rotation. The variation of transfer functions with head rotation and the improvement of the system performance by the optimization method are analyzed for three different acoustic environments, including an anechoic chamber, a normal room and a lightly damped room. The experimental results verify that the optimization method can improve the performance robustness of the active headrest to head rotation under different acoustic conditions. When the head rotates from -90 deg. to 90 deg., the minimum noise reduction at 125Hz, 250Hz and 500Hz is improved by 7.2~11.0 dB. The noise reduction of the optimized system thus remains above 10 dB in the anechoic chamber and in the ordinary room, while in the lightly damped room the noise reduction is above 6 dB due to strong reflections.

Double Panel Structure for Active Control of Noise Transmission

Passive noise reduction means are commonly used to reduce noise in the industry but, unfortunately, their effectiveness is poor in the low frequency range. By applying active structural acoustic control to the enclosure walls significant improvement of the insulating properties in this frequency range can be achieved. In this paper a model of double panel structure with ASAC is presented. The structure consists of two aluminium plates separated by an air gap. Two inertial magnetoelectric actuators and two piezoceramic MFC sensors were used for controlling the structure. A multichannel FxLMS algorithm with virtual error microphone technique is used as a control algorithm. The signal of a virtual error microphone is extrapolated basing on signals from MFC sensors. Performance of this actively controlled structure for tonal signals at selected frequencies is presented in the article. During the study, a double panel structure was mounted on one wall of sound insulating enclosure located in an acoustic chamber. During the measurements local and global reduction of noise test signal was investigated.

A Low Frequency Noise Source Localization and Identification Method Based on a Virtual Open Spherical Vector Microphone Array

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

Turbulence effects on shaped booms: Propagation simulations using KZKFourier

Upcoming X-59 aircraft flight tests as part of NASA’s Quesst Mission are expected to occur in a range of atmospheric conditions. Sensitivity of ground waveform acoustic metrics to turbulent perturbations through which booms propagate is a complicating factor in determining noise levels. A series of simulations through turbulence was executed using the KZKFourier model of Stout et al. to develop a database of results with which to expand functionality of NASA tools for estimating turbulence effects on shaped-boom ground waveforms. For 45 cases covering a seven-factor design space, multiple realizations of turbulence were generated to characterize statistical turbulence effects on levels of six acoustic metrics. Each individual simulation produced over one thousand waveforms across a virtual microphone array. Different measures were used to evaluate refinement of results with increasing number of simulations. Data suggest that mean effects and variability were most strongly influenced by propagation distance as well as velocity fluctuation intensity, and that, although local increases in acoustic metrics were common, the overall average result in all cases was a reduction in metric levels. Sensitivity of acoustic metrics varied, with mean reductions in Perceived Level occurring in the range of up to 2 dB across the 45 cases.

Machine-learning-based estimation of absorption coefficients from transfer functions modeled by equivalent sources

In the field of room acoustics, it is important to find the absorption coefficients of the wall surface, which is a boundary condition for modeling the room acoustic field. However, it is not easy to measure the acoustic impedances of the entire room because it requires many measurement points near the wall surface. Recently, a method to estimate the acoustic impedance and absorption coefficients by using both measurement and simulation methods has been proposed. However, a large number of measurement points are required to obtain sufficient estimation accuracy. In this study, we proposed estimation method of the sound absorption coefficients using machine learning with virtually increasing the number of microphones. First, the transfer functions at the virtual microphones are obtained from small number of transfer functions based on the sound field modeling by sparse equivalent sources. Then, the both transfer functions at the virtual and real microphones are used as the training data for machine learning. To evaluate estimation accuracy of the proposed method, we conducted the two-dimensional simulation experiments based on the boundary element method.

Robust estimation of open aperture active control systems using virtual sensing

For active noise control systems in an open aperture application, virtual sensing system is often needed to overcome design constraint in terms of microphone placement. The virtual sensing system, however, make assumptions to the acoustical field and thus is highly sensitive to any changes in the primary field. Taking the window aperture ANC application for example, incoming disturbance signals could impinge from multiple locations, altering the spatial correlation between the physical and virtual microphones. For instance, in the context of a high-rise apartment window, aircraft noise would propagate downwards from the sky, whereas traffic noise propagate upward from the ground. This paper considers the estimation performance of the remote microphone technique, an example of a virtual sensing method, in an open aperture application when faced with changes in the primary noise source. Additionally, this paper introduces a new method for estimating the spectral density due to multiple sources through the incoherent decomposition method. It has been shown experimentally that this algorithm is able to improve the nearfield estimation of the system.

Applying the remote microphone method in the filtered error least mean squares algorithm

An active noise control (ANC) system generates an anti-noise wave to reduce the noise level at a control point, where the error microphone is conventionally placed. Virtual sensing techniques are developed for situations when the error microphone cannot be permanently placed at the control point. The remote microphone (RM) method is one of the most straightforward virtual sensing methods. Previous studies have demonstrated that the performance of the RM method is influenced by the causality between the physical and virtual error microphones, which can be resolved by introducing a delayed version of the virtual error signal. So far, the RM method has mainly been examined with the filtered reference least mean squares (FxLMS) algorithm. This paper applies the RM method in the filtered error least mean squares (FeLMS) algorithm. The FeLMS algorithm introduces an adjoint filter to reduce the computational complexity of the ANC system. The delay incurred by the adjoint filter is just right to implement the delayed virtual error signal of the RM method.

Interpolated Individual Weighting Subband Volterra Filter for Nonlinear Active Noise Control

Nonlinear active noise control (NLANC) is always a challenge in algorithm design. Most of the NLANC algorithms are based on the linear-in-the-parameters (LIP) filters, and no work is conducted on subband adaptive filter (SAF). In this brief, a novel nonlinear SAF, termed the Volterra filtered-x normalized SAF (VFxNSAF), is proposed for NLANC, whose input vector is obtained according to the size of the analysis filter bank. Moreover, the virtual microphone strategy is applied to VFxNSAF to suppress the noise at a remote location. To mitigate the computational burden, the interpolated IWVFxNSAF with individual weighting (IW) (I-IWVFxNSAF) is proposed, in which the quadratic kernel is obtained by the interpolated filter. Simulation results corroborate the superiority of the VFxNSAF and I-IWVFxNSAF algorithms for NLANC.

Remote Microphone Technique for Active Noise Control Over Distributed Networks

Multichannel Active Noise Control (ANC) headrest systems have usually been designed with the objective of creating quiet areas at the passenger positions within the cabin of a public transport. Due to the high computational demands of dealing with multiple loudspeakers and multiple microphones by using a single centralized processor, and the convenience of a scalable system, a distributed implementation may be advisable. On the other hand, the addition of the remote microphone (RM) technique to the ANC headrest systems has recently allowed the creation of local zones of quiet at unachievable placements for the error sensors. This technique allows the quiet zone to be shifted to a desired location away from the physical sensor. However, to our knowledge, the implementation of an ANC headrest system with virtual microphones over a distributed acoustic sensor network has not been addressed. In this work, a distributed version of the multiple-error FxLMS (MEFxLMS) algorithm considering the RM method (RM-DMEFxLMS) is proposed by using an alternative formulation of the RM method. Through a set of simulations, it is found that on a non-constrained communication network, the proposed algorithm presents the same performance of its centralized version and provides a scalable and more versatile ANC system.

Synthesizing coherence loss by atmospheric turbulence in virtual microphone array signals.

Phased microphone array methods are increasingly used to localize and quantify noise sources of aircraft under flight condition. However, beamforming results suffer from loss of image resolution and corruption of sound levels due to atmospheric turbulence causing coherence loss between microphones. A synthesis method is presented that reproduces these effects in a virtual environment. Sound propagation through turbulent atmosphere is described by models by Ostashev and Wilson and by von Kármán turbulence spectra. Spatial coherence is calculated based on the parabolic equation for statistically inhomogeneous, isotropic turbulence. Decorrelation of signals is achieved by time-varying mixing of mutually independent signals with identical PSD based on coherence factors. The concept of auralization is employed to account for propagation delay, geometrical spreading, Doppler effect, air absorption, and ground effect. The application is demonstrated for a virtual 56 m aperture microphone array. The impact of different meteorological conditions on the beamforming and deconvoluted results are presented. For increasing turbulence strength, the results show decreasing sound levels and increasingly blurred images. The proposed method allows us to reproduce the effects of turbulence-induced coherence loss in phased microphone array measurements and to optimize array designs and algorithms in a virtual, controllable environment.

Spatial Upsampling of Sparse Spherical Microphone Array Signals

We present a method for spatial upsampling of signals captured with spherical microphone arrays with a limited number of microphones. The upsampling is performed by adding virtual microphone signals using interpolation between the measured array signals. For interpolation, the differences in amplitude and time of neighbored signals are weighted separately. For this, the time differences are determined with a broadband cross-correlation. After the upsampling, the array signals can be represented as spherical harmonics (SH) coefficients at significantly higher orders. This work introduces the proposed upsampling procedure and presents a technical evaluation based on simulated and measured sound fields. Thereby, we focus on the binaural decoding of the upsampled SH signals. For perceptual evaluation, we present the results of a listening experiment evaluating the binaural reproduction of the upsampled signals compared to a baseline Ambisonics decoding and the state-of-the-art decoding with Magnitude Least-Squares optimization. Both the technical and perceptual evaluations consider only array impulse responses, but the algorithm could generally be applied to array streams. The evaluation shows that the proposed method significantly improves the binaural reproduction of low-order spherical microphone array captures compared to established methods.

Speech improvement in noisy reverberant environments using virtual microphones along with proposed array geometry

This paper proposes a novel approach for improving the speech of a single speaker in noisy reverberant environments. The proposed approach is based on using a beamformer with a large number of virtual microphones with the suggested arrangement on an open sphere. Our method takes into account virtual microphone signal synthesizing using the non-parametric sound field reproduction in the spherical harmonics domain and the popular weighted prediction error method. We obtain entirely accurate beam steering towards a known source location with more directivity. The suggested approach is proven to perform effectively not just in boosting the directivity factor but also in terms of improving speech quality as measured by subjective metrics like the PESQ. In comparison to current research in the area of speech enhancement by beamformer, our experiments reveal more noise and reverberation suppression as well as improved quality in the enhanced speech samples due to the usage of virtual beam rotation in the fixed beamformer. Text for this section.

Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation

The movement and rotation of the human head can significantly degrade the control performance of active headrests with virtual microphones. This paper investigates the performance robustness of an active headrest with virtual microphones against the head rotation in a pure tone diffracted diffuse sound field. A physical model of a headrest system with a diffractive sphere is firstly developed, based on which the influence of acoustic transfer responses on the performance robustness against head rotation is analyzed. Then, the multi-objective optimization with constraints is developed to improve the performance robustness of the system, by designing optimized plant responses. Simulation results show that the method is effective in improving the robustness of single and dual channel systems. For a dual-channel headrest system with the proposed arrangements of secondary sources and microphones, the promotion of the minimum noise reduction is more than 7 dB after optimization when the head rotates from −90 degrees to 90 degrees at 125 Hz, 250 Hz and 500 Hz. In addition, the influence of different parameters on the optimization method is discussed for this dual-channel system. Finally, experiments carried out in an ordinary room validate the effectiveness of the proposed method.