The invention provides a method,device, system and apparatus for testing a microphone array and a storage medium The method comprises the following steps: receiving an audio signal obtained by collecting test audio by the microphone array; processing the audio signal to obtain a single channel designation parameter of each microphone and a channel-to-channel designation parameter between the microphones; determining the performance of the microphone array according to the single channel designated parameter and the channel-to-channel designated parameter, and outputting a performance test result; wherein, the single-channel specified parameters include sensitivity level, sensitivity level curve, total harmonic distortion parameter, total harmonic distortion curve, noise level, signal-to-noise ratio, tolerance of frequency response, tightness parameter and truncation parameter; the specified parameters among the channels include frequency response consistency parameters, time delay consistency parameters and correlation parameters Thus, the present disclosure improves the accuracy of performance test results and reduces the difficulty of performance testing of microphone arrays

Method,device, system and apparatus for testing microphone array and storage medium

Distributed IMM_unscented Kalman filter for speaker tracking in microphone array networks

Sound signals have been widely applied in various fields. One of the popular applications is sound localization, where the location and direction of a sound source are determined by analyzing the sound signal. In this study, two microphone linear arrays were used to locate the sound source in an indoor environment. The TDOA is also designed to deal with the problem of delay in the reception of sound signals from two microphone arrays by using the generalized cross-correlation algorithm to calculate the TDOA. The proposed microphone array system with the algorithm can successfully estimate the sound source’s location. The test was performed in a standardized chamber. This experiment used two microphone arrays, each with two microphones. The experimental results prove that the proposed method can detect the sound source and obtain good performance with a position error of about 2.0~2.3 cm and angle error of about 0.74 degrees. Therefore, the experimental results demonstrate the feasibility of the system.

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Sound Localization Based on Acoustic Source Using Multiple Microphone Array in an Indoor Environment

Geometry calibration for distributed acoustic sensor networks is becoming increasing popular in the signal processing community. In this paper, a distributed geometry calibration method based on network Newton distributed optimization is proposed for the acoustic transceiver networks where each node consists of a microphone array and a loudspeaker. After collecting the direction-of-arrival and time-difference-of-arrival measurements, a two-stage centralized cost function is formulated to estimate the geometrical configuration of networks, and the corresponding identifiability conditions are discussed. Next, to achieve the distributed calibration, a distributed cost function is established by splitting the centralized cost function into multiple local cost functions. Finally, the distributed geometry calibration is carried out by using network Newton distributed optimization. The proposed method can effectively estimate the geometry structure of acoustic transceiver networks in noisy and reverberant environments. Compared with the existing approaches, it implements the calibration process in a distributed manner, which requires only the local communication among nodes and does not need an external central processor. Experimental results show the validity of the proposed method.

Geometry Calibration for Acoustic Transceiver Networks Based on Network Newton Distributed Optimization

In the procedure of surface defects detection for large-aperture aspherical optical elements, it is of vital significance to adjust the optical axis of the element to be coaxial with the mechanical spin axis accurately. Therefore, a machine vision method for eccentric error correction is proposed in this article. Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an adaptive enhancement algorithm (AEA) is proposed to strengthen the crosshair image. AEA consists of the existed guided filter dark channel dehazing algorithm (GFA) and the proposed lightweight multiscale densely connected network (MDC-Net). The enhancement effect of GFA is excellent but time-consuming, and the enhancement effect of MDC-Net is slightly inferior but strongly real time. As AEA will be executed dozens of times during each correction procedure, its real-time performance is very important. Therefore, by setting the empirical threshold of definition evaluation function SMD2, GFA and MDC-Net are, respectively, applied to highly and slightly blurred crosshair images so as to ensure the enhancement effect while saving as much time as possible. AEA has certain robustness in time-consuming performance, which takes an average time of 0.2721 and 0.0963 s to execute GFA and MDC-Net separately on ten 200 pixels $\times200$ pixels region of interest (ROI) images with different degrees of blur, and also, the eccentricity error can be reduced to be within $10~\mu \text{m}$ by our method.

A Machine Vision Method for Correction of Eccentric Error Based on Adaptive Enhancement Algorithm

Geometry calibration is an inherent challenge in distributed acoustic sensor networks. To mitigate this problem, a passive geometry calibration approach based on distributed damped Newton optimization is proposed. Specifically, a geometric cost function incorporating direction of arrivals (DoAs) and time difference of arrivals (TDoAs) is first formulated, and then its identifiability conditions are given. Next, to achieve a distributed geometry calibration, the cost function is split into multiple local cost functions that are assigned to every node. After that, a distributed damped Newton optimization is presented to retrieve the geometry of microphone nodes and synchronize the internal delay between each two neighboring nodes. Finally, computational complexity and transmission bandwidth requirements are further analyzed. Compared with the existing approaches, the proposed method estimates the geometry structure of microphone networks in a distributed manner. Moreover, it requires a small number of acoustic sources. Experimental results show the validity of the proposed method.

Passive Geometry Calibration for Microphone Arrays Based on Distributed Damped Newton Optimization

Frequency response differences among microphones will result in performance degradation of microphone arrays. To remedy this problem, a frequency response calibration method based on multi-channel Wiener filters (MCWFs) is proposed in this paper. The geometric relationship of a single sound source and uncalibrated microphones is first formulated via maximum allowable errors of gain and phase. Next, the system model of the frequency response calibration is constructed, and the microphone signals are approximated to each other using the MCWF. Finally, a least-mean-square (LMS) algorithm is presented to obtain the Wiener solutions rapidly. The proposed method can effectively compensate both the gain and phase errors caused by different sensitivities among microphones in noisy and reverberant environments. The simulation and real-word experimental results reveal the validity of the proposed method.

Frequency Response Calibration Using Multi-Channel Wiener Filters for Microphone Arrays

There are many intelligent devices around us that have the ability to emit and receive audio signals, such as smartphones, smartwatches, and tablets. If they constitute an acoustic transceiver network, it can boost the performance of many audio processing tasks. The speaker localization and tracking algorithms using such a network require the prior information of node positions. To acquire this knowledge, we derive an analytical solution for geometry calibration of acoustic transceiver networks where each node consists of a microphone array and a loudspeaker. Specifically, a linear cost function for node orientations is first established using the delivered direction-of-arrival (DoA) measurements, and its analytical solution is derived. Next, based on the DoA and time-of-arrival (ToA) measurements, another cost function for node positions is formulated, which can also be solved in the closed form. Experimental results show that the proposed method can successfully estimate the geometry structure of acoustic transceiver networks in noisy and reverberant environments. In contrast to most state-of-the-art geometry calibration algorithms, which iteratively solve the calibration problem by optimization methods, the proposed method can decrease the computational complexity greatly.

Analytical Geometry Calibration for Acoustic Transceiver Arrays

Sound source localization with less data is a challenging task. To address this problem, a novel sound source localization method based on compressive sensing theory is proposed in this paper. Specifically, a sparsity basis is first constructed for each microphone by shifting the audio signal recorded from one reference microphone. In this manner, the microphones except the reference one are allowed to capture audio signals under the sampling rate far below the Nyquist criterion. Next, the source positions are estimated by solving an $$l_1$$
 minimization based on each frame of audio signals. Finally, a fine localization scheme is presented by fusing the estimated source positions from multiple frames. The proposed method can directly determine the number of sound sources in one step and successfully estimate the source positions in noisy and reverberant environments. Experimental results demonstrate the validity of the proposed method.

De Hu

Papers

Passive Geometry Calibration for Microphone Arrays Based on Distributed Damped Newton Optimization

Frequency Response Calibration Using Multi-Channel Wiener Filters for Microphone Arrays

Geometry Calibration for Acoustic Transceiver Networks Based on Network Newton Distributed Optimization

Analytical Geometry Calibration for Acoustic Transceiver Arrays

Compressive Sensing-Based Sound Source Localization for Microphone Arrays