Active microphonics noise suppression based on DOB control in 166.6-MHz superconducting cavities for HEPS

Purpose Superconducting 166.6-MHz cavities will be used to accelerate electron beams in high-energy photon source(HEPS).The radio-frequency(RF)fields inside these cavities have to be controlled better than 0.03%(rms error)for the amplitude and 0.03◦(rms error)for the phase.Adopting a quarter-wave geometry withβ=1,the 166.6-MHz cavity has two intrinsic mechanical modes at∼100Hz observed in both simulations and cryogenic tests.If coupled to external vibrations,these microphonics modes shall stress the existing proportional–integral(PI)feedback controller and inevitably deteriorate the field stabilities.Therefore,additional noise suppression may be required.Methods Adigital low-level RF system previously in-house developedwas connected to a 166.6-MHz dressed cavity at room temperature in the laboratory.Piezo-tunerswere used to“knock”on the cavity at various frequencies to excite cavity vibrations,and microphonics spectrum was subsequently measured.A disturbance observer(DOB)-based algorithm was adopted and integrated into the existing feedback controller.The performance of PI controller,DOB controller and a combination of PI and DOB controller was compared.The limitation of the DOB controller was also examined.Results and conclusions The PI controller was proved to be insufficient in suppressing large cavity microphonics during the tests.By adding the DOB controller,the excellent field stabilities can be restored.Optimized loop parameters were obtained.The simple first-order filter was adequate thanks to the robustness of the DOB controller.This constitutes a first laboratory demonstration of the active microphonics noise suppression in the 166.6-MHz RF cavity for HEPS.

Speech Separation Algorithm Using Gated Recurrent Network Based on Microphone Array

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

Fabrication of Silicon Condenser Microphone Using Oxidized Porous Silicon as Sacrificial Layer

A new technique to fabricate silicon condenser microphone is presented.The technique is based on the use of oxidized porous silicon as sacrificial layer for the air gap and the heavy p+-doping silicon of approximately 15μm thickness for the stiff backplate.The measured sensitivity of the microphone fabricated with this technique is in the range from -45dB(5.6mV/Pa) to -55dB(1.78mV/Pa) under the frequency from 500Hz to 10kHz,and shows a gradual increase at higher frequency.The cut-off frequency is above 20kHz.

光纤microphone的理论与实验研究

本文提出了一种新型的反射式光纤ｍｉｃｒｏｐｈｏｎｅ ，它把反射式光纤传感探头应用于传统的麦克风上，来实现对声波的调制．本文从理论和实验两方面给出了反射式光纤ｍｉｃｒｏｐｈｏｎｅ的光强调制函数，并对反射式光纤ｍｉｃｒｏｐｈｏｎｅ系统进行了研究．

Experimental study on spectrum and multi-scale nature of wall pressure and velocity in turbulent boundary layer

When using a miniature single sensor boundary layer probe, the time sequences of the stream-wise velocity in the turbulent boundary layer （TBL） are measured by using a hot wire anemometer. Beneath the fully developed TBL, the wall pressure fluctuations are attained by a microphone mechanism with high spatial resolution. Analysis on the statistic and spectrum properties of velocity and wall pressure reveals the relationship between the wall pressure fluctuation and the energy-containing structure in the buffer layer of the TBL. Wavelet transform shows the multi-scale natures of coherent structures contained in both signals of velocity and pressure. The most intermittent wall pressure scale is associated with the coherent structure in the buffer layer. Meanwhile the most energetic scale of velocity fluctuation at y＋ = 14 provides a specific frequency f9 ≈ 147 Hz for wall actuating control with Ret = 996.

STUDY ON FLAP SIDE-EDGE NOISE BASED ON THE FLY-OVER MEASUREMENTS WITH A PLANAR MICROPHONE ARRAY

A large planar microphone array, which consists of 111 microphones, was successfully applied to measure a two dimensional mapping of the sound sources on landing aircraft. The focus was on the flap side edge noise source in this paper. The spectra, directivity and sound pressure level of flap side edge noise of 10 aircraft were presented in this paper. It is found that the spectrum of flap side edge noise is a broadband noise with some tones in some cases. Two different types of tone sources are found. It is proposed that one type of these tone sources is trailing edge semi baffled dipole source, and another is produced from the shedding of vortex from the wing cusp. The total sound pressure level of flap side edge broadband noise has no obvious directionality. However, the directivity of the tone noise in the flap side edge noise spectrum is obvious. It is demonstrated that the local flow field is the key to controlling the flap side edge noise.

Microphone Array Speech Enhancement Based on Tensor Filtering Methods

This paper proposes a novel microphone array speech denoising scheme based on tensor filtering methods including truncated HOSVD(High-Order Singular Value Decomposition), low rank tensor approximation and multi-mode Wiener filtering. Microphone array speech signal is represented in three-order tensor space with channel, time, and spectrum modes and then tensor filtering model can be designed to process the multiway array data. As to the first method, noise can be reduced through the truncated HOSVD which is a simple scheme in tensor processing. It is more accurate to find the lower-rank approximation of the three-order tensor with Tucker model. Then MDL(Minimum Description Length) criterion is used to estimate the optimal tensor rank in the second method. Further, multimode Wiener filtering approach upon tensor analysis can be considered as the spanning of one-mode wiener filtering. How to take advantages of tensor model to obtain a set of filters is the heart of the novel scheme. The performances of the proposed three approaches are evaluated with objective indexes and listening quality test. The experimental results indicate that the proposed tenor filtering methods have potential ability of retrieving the target signal from noisy microphone array signal and the multi-mode Wiener filtering method provides the best denoising results among the three ones.

Sound Source Localization Based on SRP-PHAT Spatial Spectrum and Deep Neural Network

Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic scenarios.To address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transform(SRP-PHAT)spatial spectrum as input feature is presented in this paper.Since the SRP-PHAT spatial power spectrum contains spatial location information,it is adopted as the input feature for sound source localization.DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features.SRP-PHAT at each steering position within a frame is arranged into a vector,which is treated as DNN input.A DNN model which can map the SRP-PHAT spatial spectrum to the azimuth of sound source is learned from the training signals.The azimuth of sound source is estimated through trained DNN model from the testing signals.Experiment results demonstrate that the proposed algorithm significantly improves localization performance whether the training and testing condition setup are the same or not,and is more robust to noise and reverberation.

Theoretical Investigation of the Vortex Shedding Noise from the Wake of Airfoil

The vortex shedding noise has been revealed as an important wing noise source on some modern commercial aircraft based on the fly-over measurements with a planar microphone array by Michel (1998). In this paper, an analytical model is presented for predicting this vortex shedding noise. The downstream wake of a 2-dimensional airfoil is assumed to be dominated by the von Karman vortex street, and the strength and the shedding frequency of the wake vortex are determined from the wake structure model. An aero-acoustic model is developed based on the Howe's unified theory of trailing edge noise and is incorporated with the wake model to predict the sound pressure level and directivity of vortex shedding noise. The predicted vortex shedding frequencies, sound pressure levels and directivities compare favorably with the measured results for 6 modern commercial aircraft.

Enhanced Frequency-Domain Frost Algorithm Using Conjugate Gradient Techniques for Speech Enhancement

In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless response （MVDR） solution with the correlation matrix inversion, the Frost algorithm implementing the stochastic constrained least mean square （LMS） algorithm can adaptively converge to the MVDR solution in mean-square sense, but with a very slow convergence rate. In this paper, we propose a frequency-domain constrained conjugate gradient （FDCCG） algorithm to speed up the convergence. The devised FDCCG algorithm avoids the matrix inversion and exhibits fast convergence. The speech enhancement experiments for the target speech signal corrupted by two and five interfering speech signals are demonstrated by using a four-channel acoustic-vector-sensor （AVS） micro-phone array and show the superior performance.

Source localization with minimum variance distortionless response for spherical microphone arrays

To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield in- formation. For the far field sound sources, the array signal model is constructed based on plane wave decomposition. The spatial spectrum function is calculated by minimum variance distortionless response （MVDR） to scan the three-dimensional space. The peak values of the spectrum function correspond to the directions of multiple sound sources. A diagonal loading method is adopted to solve the ill-conditioned cross spectrum matrix of the received signals. The loading level depends on the alleviation of the ill-condition of the matrix and the accuracy of the inverse calculation. Compared with plane wave decomposition method, our proposed localization algorithm can acquire high spatial resolution and better estimation for multiple sound source directions, especially in low signal to noise ratio （SNR）.

Modelling and Optimisation of a Spring-Supported Diaphragm Capacitive MEMS Microphone

Audio applications such as mobile communication and hearing aid devices demand a small size but high performance, stable and low cost microphone to reproduce a high quality sound. Capacitive microphone can be designed to fulfill such requirements with some trade-offs between sensitivity, operating frequency range, and noise level mainly due to the effect of device structure dimensions and viscous damping. Smaller microphone size and air gap will gradually decrease its sensitivity and increase the viscous damping. The aim of this research was to develop a mathematical model of a spring-supported diaphragm capacitive MEMS microphone as well as an approach to optimize a microphone’s performance. Because of the complex shapes in this latest type of diaphragm design trend, analytical modelling has not been previously attempted. A novel diaphragm design is proposed that offers increased mechanical sensitivity of a capacitive microphone by reducing its diaphragm stiffness. A lumped element model of the spring-supported diaphragm microphone is developed to analyze the complex relations between the microphone performance factors and to find the optimum dimensions based on the design requirements. It is shown analytically that the spring dimensions of the spring-supported diaphragm do not have large effects on the microphone performance com pared to the diaphragm and backplate size, diaphragm thickness, and air-gap distance. A 1 mm2 spring-supported diaphragm microphone is designed using several optimized performance parameters to give a –3 dB operating bandwidth of 10.2 kHz, a sensitivity of 4.67 mV/Pa (–46.5 dB ref. 1 V/Pa at 1 kHz using a bias voltage of 3 V), a pull-in voltage of 13 V, and a thermal noise of –22 dBA SPL.

A FAST SEARCH METHOD OF STEERED RESPONSE POWER WITH SMALL-APERTURE MICROPHONE ARRAY FOR SOUND SOURCE LOCALIZATION

The Steered Response Power(SRP)method works well for sound source localization in noisy and reverberant environment.However,the large computation complexity limits its practical application.In this paper,a fast SRP search method is proposed to reduce the computational complexity using small-aperture microphone array.The proposed method inspired by the SRP spatial spectrum includes two steps:first,the proposed method estimates the azimuth of the sound source roughly and determines whether the sound source is in far field or near field;then,different fine searching operations are performed according to the sound source being in far field or near field.Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computation complexity of the proposed method with those of the conventional SRP-PHAT algorithm.The results show that,the proposed method has a comparative accuracy with the conventional SRP algorithm,and achieves a reduction of 93.62%in computation complexity compared to the conventional SRP algorithm.

Noise Source Identification Applied in Electric Power Industry Using Microphone Arrays

The noise source identification is an important issue in noise reduction and condition monitoring(CM) for machines in- site using microphone arrays. In this paper, we propose a new approach to optimize array configuration based on particles swarm optimization algorithm in order to improve noise source identification and condition monitoring performance. Two distinct optimized array configurations are designed under the certain conditions. Furthermore, an acoustic imaging equipment is developed to carry out experiments on transformer substation equipment and wind turbine generator, which demonstrate that the acoustic imaging system allows a high resolution in identifying main noise sources for noise reduction and abnormal noise sources for condition monitoring.

广播电视节目录制中专业话筒的正确选择和使用

在广播电视节目录制过程中,话筒是声音的入口,它的性能、质量直接影响收录声音的效果,其音色特点也必然带到拾音里去,如果产生瑕疵,那么在以后的音质修饰和加工中几乎无法弥补。主要讲述了广播级话筒的各项主要指标和参数,包括声压、灵敏度、最大输出电平等常用数据,和广播电视台采访录音制作中常用话筒的分类、特点,以及在不同的录音环境中正确选择合适的话筒和正确的使用方法。

Microphone Array-Based Sound Source Localization Using Convolutional Residual Network

Microphone array-based sound source localization(SSL)is widely used in a variety of occasions such as video conferencing,robotic hearing,speech enhancement,speech recognition and so on.The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments.In order to improve localization performance,a novel SSL algorithm using convolutional residual network(CRN)is proposed in this paper.The spatial features including time difference of arrivals(TDOAs)between microphone pairs and steered response power-phase transform(SRPPHAT)spatial spectrum are extracted in each Gammatone sub-band.The spatial features of different sub-bands with a frame are combine into a feature matrix as the input of CRN.The proposed algorithm employ CRN to fuse the spatial features.Since the CRN introduces the residual structure on the basis of the convolutional network,it reduce the difficulty of training procedure and accelerate the convergence of the model.A CRN model is learned from the training data in various reverberation and noise environments to establish the mapping regularity between the input feature and the sound azimuth.Through simulation verification,compared with the methods using traditional deep neural network,the proposed algorithm can achieve a better localization performance in SSL task,and provide better generalization capacity to untrained noise and reverberation.

A New Calibration Method for Microphone Array with Gain, Phase, and Position Errors

Microphone array can be used in sound source localization and separation. But gain, phase, and position errors can seriously influence the performance of localization algorithms such as multiple signal classification （MUSIC） algorithm. In this paper, a new calibration method for microphone array with gain, phase, and position errors is proposed. Unlike traditional calibration methods for antenna array, the proposed method can be used in the broadband and near-field signal model such as microphone array with arbitrary sensor geometries in one plane. Computer simulations are presented and simulation results show the new method having good performance.

Posture Adjustment of Microphone Based on Image Recognition in Automatic Welding System

As the requirements of production process is getting higher and higher with the reduction of volume,microphone production automation become an urgent need to improve the production efficiency.The most important part is studied and a precise algorithm of calculating the deviation angle of four types microphones is proposed,based on the feature extraction and visual detection.Pretreatment is performed to achieve the real-time microphone image.Canny edge detection and typical feature extraction are used to distinguish the four types of microphones,categorizing them as type M1 and type M2.And Hough transformation is used to extract the image features of microphone.Therefore,the deviation angle between the posture of microphone and the ideal posture in 2Dplane can be achieved.Depending on the angle,the system drives the motor to adjust posture of the microphone.The final purpose is to realize the high efficiency welding of four different types of microphones.

A Robust and Efficient Compressed Sensing Algorithm for Wideband Acoustic Imaging

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.

Wave Superposition Based Sound Field Reconstruction

In order to overcome the obstacle of singular integral in boundary element method （BEM）, we presented an efficient sound field reconstruction technique based on the wave superposition method （WSM）. Its principle includes three steps： first, the sound pressure field of an arbitrary shaped radiator is measured with a microphone array; then, the exterior sound field of the radiator is computed backward and forward using the WSM; at last, the final results are visualized in terms of sound pressure contours or animations. With these visualized contours or animations, noise sources can be easily located and quantified; also noise transmission path can be found out. By numerical simulation and experimental results, we proved that the technique are suitable and accurate for sound field reconstruction. In addition, we presented a sound field reconstruction systern prototype on the basis of this technique. It makes a foundation for the application of wave superposition in the sound field reconstruction in industry situations.