Calibration of acoustic vector sensor based on MEMS microphones for DOA estimation

Kotus, Józef; Szwoch, Grzegorz

doi:10.1016/j.apacoust.2018.07.025

Cited by 27 publications

(16 citation statements)

References 10 publications

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“…A final conformity of transducers (removing the discrepancies between them) is obtained by digital filtering. This approach is presented, for example, in the works [14][15][16]. Like any measurement method, despite its many advantages, it also has some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Cost-Effective Miniature Microphone Sound Intensity 2D Probe

Mickiewicz

Raczyński

Parus

2020

Sensors

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This article presents the functional properties of modified versions of the 2D pressure–pressure intensity probe allowing us to determine the vector of sound intensity on a plane using a mechatronic system with one or two miniature electret microphones. The introduction contains basic information about the application areas of the sound intensity and its measurement problems. Next, the principle of operation of the probes and the construction of the prototype measurement system are described. It was subjected to comparative analysis for the stability of obtained results and accuracy of directional characteristics in free field conditions. For this purpose, experiments were conducted to analyze the flow of acoustic power in an anechoic chamber using both (one- and two-microphone) probes. The results were used for a comparative metrological analysis of the described methods and to indicate the advantages and disadvantages of both constructions. The next part of the article presents an experiment concerning the measurement of the sound intensity impulse response of a room, which is an example of practical use of the probe to analyze reflections in the room, which can be used in sound engineering and architectural acoustics.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Cost-Effective Miniature Microphone Sound Intensity 2D Probe

Mickiewicz

Raczyński

Parus

2020

Sensors

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“…The correction functions were obtained by means of the calibration procedure, performed in an anechoic chamber. One amplitude correction function for each pressure signal (each microphone) and one phase correction function for each axis (each acoustic velocity signal) were calculated [ 29 ]. The correction was applied to the signals during the calculations, using finite impulse response digital filters of length 512.…”

Section: Resultsmentioning

confidence: 99%

“…Obtaining accurate azimuth and elevation values from the AVS requires that all pressure signals are aligned in amplitude, and the velocity signals are aligned in phase. This is ensured by means of the calibration procedure that calculates the amplitude and phase correction functions [ 29 ]. These functions are applied during the intensity measurement.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we assume that the pressure signals are discrete and uniformly sampled. The pre-processing stage performs amplitude and phase equalization of the pressure signals to minimize differences between the transfer functions of microphones that would contribute to errors in sound intensity measurements [ 29 ]. Next, signal sections that represent individual vehicles are determined by analysis of the intensity and azimuth functions [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

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Estimation of Average Speed of Road Vehicles by Sound Intensity Analysis

Kotus

Szwoch

2021

Sensors

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Constant monitoring of road traffic is important part of modern smart city systems. The proposed method estimates average speed of road vehicles in the observation period, using a passive acoustic vector sensor. Speed estimation based on sound intensity analysis is a novel approach to the described problem. Sound intensity in two orthogonal axes is measured with a sensor placed alongside the road. Position of the apparent sound source when a vehicle passes by the sensor is estimated by means of sound intensity analysis in three frequency bands: 1 kHz, 2 kHz and 4 kHz. The position signals calculated for each vehicle are averaged in the analysis time frames, and the average speed estimate is calculated using a linear regression. The proposed method was validated in two experiments, one with controlled vehicle speed and another with real, unrestricted traffic. The calculated speed estimates were compared with the reference lidar and radar sensors. Average estimation error from all experiment was 1.4% and the maximum error was 3.2%. The results confirm that the proposed method allow for estimation of time-averaged road traffic speed with accuracy sufficient for gathering traffic statistics, e.g., in a smart city monitoring station.

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“…Then the second step of the correction is performed in which phase differences between the particle velocity and the average acoustic pressure signals are equalized (P.Corr). The authors' previous publication described the detailed description of the calibration and correction process [21].…”

Section: Acoustic Vector Sensormentioning

confidence: 99%

Estimating Traffic Intensity Employing Passive Acoustic Radar and Enhanced Microwave Doppler Radar Sensor

2019

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Innovative road signs that can autonomously display the speed limit in cases where the traffic situation requires it are under development. The autonomous road sign contains many types of sensors, of which the subject of interest in this article is the Doppler sensor that we have improved and the constructed and calibrated acoustic probe. An algorithm for performing vehicle detection and tracking, as well as vehicle speed measurement, in a signal acquired with a continuous wave Doppler sensor, is discussed. A method is also experimentally presented and studied for counting vehicles and for determining their movement direction by means of acoustic vector sensor application. The assumptions of the method employing spatial distribution of sound intensity determined with the help of an integrated three-dimensional (3D) sound intensity probe are discussed. The enhanced Doppler radar and the developed sound intensity probe were used for the experiments that are described and analyzed in the paper.

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Calibration of acoustic vector sensor based on MEMS microphones for DOA estimation

Cited by 27 publications

References 10 publications

Performance Analysis of Cost-Effective Miniature Microphone Sound Intensity 2D Probe

Performance Analysis of Cost-Effective Miniature Microphone Sound Intensity 2D Probe

Estimation of Average Speed of Road Vehicles by Sound Intensity Analysis

Estimating Traffic Intensity Employing Passive Acoustic Radar and Enhanced Microwave Doppler Radar Sensor

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