Microphone array geometry optimization for traffic noise analysis

Bjelić, Miloš; Stanojević, Miodrag; Pavlovic, Dragana Sumarac; Mijic, Miomir

doi:10.1121/1.4982694

Cited by 23 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One fundamental issue needs to be overcome: the limited amount of microphones and speakers in a smartphone. Acoustic imaging usually uses the speaker and microphone array as the transmitter and receiver [22]. However, the number of microphones and speakers in a smartphone is usually less than five.…”

Section: Theory Of Smartphone-based Acoustic Imaging Technologymentioning

confidence: 99%

Acoustic Imaging Using the Built-In Sensors of a Smartphone

Wang

Ding

et al. 2021

Symmetry

View full text Add to dashboard Cite

Thanks to the rapid development of the semiconductor industry, smartphones have become an indispensable part of our lives with their increasing computational power, 5G connection, multiple integrated sensors, etc. The boundary of the functionalities of a smartphone is beyond our imagination again and again as the new smartphone is introduced. In this work, we introduce an acoustic imaging algorithm by only using the built-in sensors of a smartphone without any external equipment. First, the speaker of the smartphone is used to emit sound waves with a specific frequency band. During the movement of the smartphone, the accelerometer collects acceleration data to reconstruct the trajectories of the movements, while the microphones receive the reflected waves. A microphone plus an accelerometer are able to partially replace the functionality of a microphone array and to become a symmetry-imitation system. After scanning, a series of algorithms are implemented to generate a heat map, which outlines the target object. Our algorithm demonstrates the feasibility of smartphone-based acoustic imaging with minimal equipment complexity and no additional cost, which is beneficial to the promotion and popularization of acoustic imaging technology in daily applications.

show abstract

Section: Theory Of Smartphone-based Acoustic Imaging Technologymentioning

confidence: 99%

Acoustic Imaging Using the Built-In Sensors of a Smartphone

Wang

Ding

et al. 2021

Symmetry

View full text Add to dashboard Cite

show abstract

“…Previous studies have addressed the optimization of microphone array geometry. In the case of frequency-domain beamforming using planar microphone arrays, noise source map metrics such as the Main Lobe Width (MLW) or the Maximum Side lobe Level (MSL) can be optimized [16,17]. Planar spiral shaped arrays are a common tradeoff [18,19,20] because they allow for reducing side lobes amplitude.…”

Section: Introductionmentioning

confidence: 99%

“…The colorbar is in dB. (Color online)function[16,17]. Reducing the MLW may affect the MSL and conversely.A multi-objective optimization can be used to tackle this problem at the expense of a more complex computation.…”

mentioning

confidence: 99%

Optimization of a spherical microphone array geometry for localizing acoustic sources using the generalized cross-correlation technique

Padois

Doutres

Sgard

et al. 2019

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

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 semianechoic chamber with prototyped spherical microphone arrays confirming that the optimized microphone array improves the quality of the noise source map.

show abstract

“…Kodrasi et al [30] adopted different heuristic optimization approaches and an exhaustive search approach to optimize the microphone positions for an arbitrary planar array based on the beamforming method, and Kodrasi’s methods found near-optimal configurations. Recently, Yan and Ma [31], Sarradj [32], Bjelić et al [33], Teng and Lv [34], and Le Courtois et al [35] also proposed new methods for planar array optimization based on the beamforming method, and compared the array performance under different localization scenarios. In the optimization procedure, the main-lobe width and side-lobe level are generally selected as the optimization objective function.…”

Section: Introductionmentioning

confidence: 99%

“…However, these array optimization methods are mainly based on the beamforming and acoustic holography methods. The optimization procedure is also usually based on existing array structures, such as cross array [38], circle array [31], spiral array [32], irregular planar array [33,34], spherical array [38], and so on. As such, certain constraints for the array structure have been introduced to the optimization.…”

Section: Introductionmentioning

confidence: 99%

Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios

Liu

Kirubarajan

Xiao

2019

Sensors

View full text Add to dashboard Cite

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

show abstract

Microphone array geometry optimization for traffic noise analysis

Cited by 23 publications

References 13 publications

Acoustic Imaging Using the Built-In Sensors of a Smartphone

Acoustic Imaging Using the Built-In Sensors of a Smartphone

Optimization of a spherical microphone array geometry for localizing acoustic sources using the generalized cross-correlation technique

Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios

Contact Info

Product

Resources

About