Sound source localization is essential for machine-based environmental perception when visual methods are not applicable. Current array-based localization methods often require many microphones and ...are heavy in computing. This article proposes an efficient algorithm for estimating the location of a single source in 3-D based on the time difference of arrival and interaural level difference (TDOA-ILD) with an array of only three microphones. The algorithm can compute the source 3-D coordinates with a closed-form analytical solution derived from geometrical modeling and identify the actual source from the cone of the confusion area through clustering methods. This article comprehensively evaluates the algorithm's behavior for various array sizes and source locations in a wide range of signal-to-noise ratios (SNRs) with simulative models to prove that the algorithm achieves estimation errors close to its Cramer-Rao lower bound (CRLB). Experimental evaluation demonstrates the method's localization feasibility in outdoor open environments with off-the-shelf sensing equipment. The proposed scheme is suitable for real-time 3-D sound source localization with cost-effective hardware.
Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is ...the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.
This letter proposes a new method for source and microphone localization in reverberant environments using a randomly arranged sensor array, under the hypothesis that the position of one reference ...sensor and the geometry of the environment are known, and the other microphone positions are unknown. A minimum mean square error (MMSE) estimator that exploits early reflections is proposed. The MMSE estimator is solved by a grid search method that combines the information on early reflections estimated using a multichannel blind system identification and the time difference of arrivals between the reflections and the direct-path calculated with the image-source model. Simulations under different reverberant scenarios demonstrate the ability of the proposed approaches in localizing source and microphone.
This letter proposes a differential error feedback active noise control (FBANC) system in an open end duct to mitigate interferences from its downstream. The interference waves entering the duct ...cause disturbance to the conventional FBANC controller and even result in divergence of the control filter. This is due to the omni-directivity of the error microphone. The differential microphone array (DMA) can be constructed in a compact size using only one more omni-directional microphone. The DMA forms a frequency-invariant beampattern that presents configurable nulls. When the output of the DMA is used as the error signal, the null can be designed to enhance the robustness of the FBANC system against interferences. However, this differential error signal is converted from the sound pressure gradient instead of the sound pressure. The DMA's location does not precisely indicate the control point where optimum noise reduction has been achieved. To solve this problem, an auxiliary filter based mapping (AFMap) method is developed to map the differential error signal to the location of an omni-directional microphone in the DMA. Experiment results demonstrate that the proposed differential error FBANC system is much less sensitive to interferences than the conventional FBANC system, and the AFMap method can ensure optimum noise reduction occurring at the target control point.
Near-field acoustic holography (NAH) has become an effective tool for acoustic source identification. It usually requires many microphones near the source surface to catch sufficient evanescent waves ...for good spatial resolution. Besides, such a measurement is time-consuming and uneconomical in practice; standard measurements using densely and uniformly populated sensors are sometimes impossible when dealing with massive sources. Eventually, the compressive sensing (CS) technique has been suggested for NAH, which employs a sparse number of sensors. Most existing works in NAH based on compressive sensing theory have focused on developing acoustic models and algorithms. In contrast, less attention has been paid to the best practice of sensor placement. The present study aims to propose and validate the methods for the optimal selection of sparse measuring points using the compression technique for NAH. Due to the small number of measuring sensors, the transfer matrix between source and hologram points becomes underdetermined. Under such a data condition, even though every piece of data would be felt precious for the reconstruction, the information from some sensors should be discarded further because not all data provide meaningfully independent information. To this end, effective independence and singular value monitoring (SVM) methods are used to test the selection of the optimal sensor position from a set of candidate positions. The former chooses nonredundant measurement data, whereas the latter keeps the low condition number of the transfer matrix. Simulation results with vibrating plates show that optimized sensor positions could effectively facilitate the optimality of the approximate solution for CS theory compared to uniformly or randomly distributed measurements. It is because the established transfer matrix has the lowest condition number due to being sparser and more independent sensor positions. Experimental results exhibit a better reconstruction error (10%-40%) at 200-2000 Hz when the sensor positions are optimized than the uniform or random selection approaches.
Microphone channel frequency response calibration (MCFRC) can compensate for frequency response mismatches among channels and has attracted increasing attention in recent years, which is accompanied ...by booming applications of microphone arrays. State-of-the-art MCFRC techniques translate the frequency response calibration (FRC) into the optimization of calibration filter coefficients and have shown remarkable performance. However, a compromise between inter-microphone distance and passband bandwidth shrinkage still exists, which limits the wide application of those techniques to a great extent. To address this problem, an MCFRC method using circular shifting and spatio-temporal prediction is proposed in this article. First, all microphones from uncalibrated channels are deployed in a rotating symmetric arrangement, and circular shifting is conducted, making each channel experience all different predetermined positions. Then, for each channel, a spatio-temporal prediction technique is utilized to separately enhance those segments containing received calibration signals at different positions. Next, the summation of all enhanced segments for each channel is calculated, which can almost ensure that differences among microphone channel (MC) outputs are only caused by channel frequency response mismatches. Finally, the Newton algorithm is employed to design the calibration filter coefficients corresponding to each channel. Simulation results reveal that the proposed approach considerably outperforms existing calibration methods across various signal-to-noise ratio (SNR), reverberation time, and inter-microphone distance conditions. Meanwhile, the passband bandwidth shrinkage problem is well handled. Real-world experiments also verify its effectiveness.
•Acoustic beamforming for noise source localization is analysed.•Key concepts of beamforming, from basics to advanced concepts, are presented.•Practical examples in different scenarios are also ...provided.
This paper is a review on acoustic beamforming for noise source localization and its applications. The main concepts of beamforming, starting from the very basics and progressing on to more advanced concepts and techniques, are presented, in order to give the reader the possibility to identify concepts and references which might be useful for her/his work. Practical examples referring to application of this technique in different scenarios are also provided. The aim is to make the reader comfortable with the topic and aware of the wide stimuli a technique like acoustic beamforming can offer researchers.
Acoustic microelectromechanical system (MEMS)-based sensors (i.e., MEMS microphones) are often operated in harsh and dusty environments where their main components of thin membranes are prone to ...damage caused by impacting physical solid objects (e.g., airborne particles and hairs). In this work, glass micromesh chips were fabricated using laser-induced deep etching (LIDE) technology on 8-inch-wafer-level scale, which can then be potentially used to protect the MEMS microphones from those environmental interferences. During device development, finite element method (FEM) simulation was conducted to optimize the micromesh design and comprehend its effect on the electroacoustic performance of glass mesh-integrated MEMS microphones. Being mounted directly on top of a packaged digital capacitive MEMS microphone sound port, the glass micromesh chip could yield a low signal-to-noise ratio (SNR) loss of (0.65 ± 0.05) dB(A) and a low sensitivity change of (0.11 ± 0.04) dBFS in electroacoustic measurements. Consequently, a high SNR value of the MEMS microphone was able to be maintained at (71.24 ± 0.11) dB(A). Owing to their low production cost on industrial scale, good environmental protection level, outstanding intrinsic material properties, and high acoustic performance, the developed glass micromesh chips can pave the way for enhancing the robustness of packaged MEMS microphones and being employed in future acoustic sensor technologies.
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•Detailed description of lumped-parameter equivalent circuits for moving-coil microphone.•The influence of sound scattering to the microphone has been considered.•Comprehensive ...predictions for microphone characteristics, such as sensitivity, impedance, directivity and proximity effect.•Good agreement between the simulation and measurement up to frequencies at 13 kHz.
This article presents a lumped-parameter model combined with external sound pressure field simulation, based on the boundary element method (BEM) for utilization in a directional microphone. A study about microphone capsule of a typical handheld moving-coil microphone with hyper-cardioid pattern has been conducted. In the BEM simulation, a simplified 3D model of the capsule that met sound waves which radiated from a point sound source has been taken into account. The blocked average pressure at the openings of the capsule is used to drive the lumped element equivalent circuit model. Through the model, the sensitivity, directivity, and impedance of the microphone were analyzed. Hence, the proximity effect is observed and discussed. To verify our simulation, the microphone characteristics were experimentally measured by B&K devices with SoundCheck software in an anechoic chamber. The simulation data are in good agreement with the measurement data at low and medium frequencies below 13 kHz, and there were larger errors at higher frequencies, which may indicate that the lumped parameters is less applicable at higher frequencies. The proposed method gains more precise and comprehensive prediction for the microphone characteristics, which therefore could be utilized in optimizing the choice of materials and size of the microphone capsule design.
Polynomial beamformers for microphone arrays, which employ the well-known Farrow structure in digital filters, have drawn interest in recent years due to their capability of dynamic beam steering via ...simple online parameter tuning. Nevertheless, the computational complexity of the polynomial beamformers is higher than that of the nonsteerable counterpart. Moreover, the computational burden will become more demanding when used with the conventional uniform-spaced arrays for high-quality sound signal acquisition, because a large number of sensors are required due to the limitation imposed by the spatial Nyquist criterion. To address the problem, in this paper we propose to design sparse polynomial beamformers by jointly sparsifying sensor locations and Farrow structures. However, the joint sparse design problem is rather challenging due to the complex structure of the polynomial beamformers. We propose an efficient algorithm to solve the high-dimensional joint sparse design problem using the alternating direction method of multipliers (ADMM). Under the ADMM framework, we first reduce the original high-dimensional optimization problem into a set of subproblems much easier to solve. Then, we theoretically derive the analytical solutions to the subproblems, which are the key to the proposed ADMM algorithm. It is shown that the proposed design algorithm has a much lower computational complexity than the convex programming based optimization approach widely-employed in sparse array design. The effectiveness of the proposed joint sparse design is evaluated by the design examples as well as through its application in speech enhancement.
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