Microphone Array Analysis Methods Using Cross-Correlations

Abstract

Due to civilian noise complaints and damage claims, there is a need to establish an accurate record of impulse noise generated at military installations. Current noise monitoring systems are susceptible to false positive detection of impulse events due to wind noise. In order to analyze the characteristics of noise events, multiple channel data methods were investigated. A microphone array was used to collect four channel data of military impulse noise and wind noise. These data were then analyzed using cross-correlation functions to characterize the input waveforms. Four different analyses of microphone array data are presented. A new value, the min peak correlation coefficient, is defined as a measure of the likelihood that a given waveform originated from a correlated noise source. Using a sound source localization technique, the angle of incidence of the noise source can be calculated. A method was also developed to combine the four individual microphone channels into one. This method aimed to preserve the correlated part of the overall signal, while minimizing the effects of uncorrelated noise, such as wind. Lastly, a statistical method called the acoustic likelihood test is presented as a method of determining if a signal is correlated or not.

Comparative Study on Non‐Gaussian Characteristics of Wind Pressure for Rigid and Flexible Structures

“…Owing to the complex wave structure of the membrane structure, the wind pressures of some measuring points like #9, #19, and #14 reveal negative values as shown in Figure 6, simultaneously leading to the negative correlation of the wind pressure in some areas. Second, the maximum of the cross-correlation function or the minimum of the negative correlation indicates the point in time where the signals are best aligned; that is, the time delay between the two signals is determined by the argument of the extreme value [38]; Figures 21(a) and 21(b) show the obvious delay time between the wind pressures of the measuring points due to the long distance and the changing wind direction. However, the delay time between the wind pressures of the measuring points in Figure 21(c) is relatively close to zero, which means that the wind simultaneously arrived at the BC side measuring points.…”

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

“…Wind noise is generated by the pressure variations from flow turbulence deflect the microphone diaphragm [10]. Therefore, the wind turbulence between microphones is comparatively uncorrelated, which can be used to distinguish the sound waves from the wind noise [11,12]. A min peak correlation coefficient measure was designed in [12] for wind noise analysis using large aperture array with radius of one meter.…”

“…Therefore, the wind turbulence between microphones is comparatively uncorrelated, which can be used to distinguish the sound waves from the wind noise [11,12]. A min peak correlation coefficient measure was designed in [12] for wind noise analysis using large aperture array with radius of one meter. Yet the low correlation is not a reliable feature for non-stationary wind noise detection with SAMA [10].…”

A quantitative method for evaluating numerical simulation accuracy of time-transient Lamb wave propagation with its applications to selecting appropriate element size and time step

“…51 Ó 2015 Published by Elsevier B.V. 52 53 54 55 1. Introduction 56 Compared with point-to-point inspection using traditional 57 ultrasonic bulk waves, Lamb wave technique provides a 58 cost-effective inspection method in non-destructive evaluation 59 (NDE) and structural health monitoring (SHM), as Lamb wave is 60 able to travel a very long distance with little energy loss and can 61 be used to interrogate physically inaccessible areas of structures 62 and components. However, due to the multi-mode characteristics 63 and dispersive nature, Lamb wave propagation behavior is much 64 more complex than that of bulk waves.…”

“…Cross correlation 122 analysis is used to measure the similarity between two signals at 123 different times. Although cross correlation analysis has been 124 widely used in many fields [57][58][59] …”