Investigating uncertain geometries effect on sound propagation in a homogeneous and non-moving atmosphere over an impedance ground

Parry, Jordan A.; Horoshenkov, Kirill V.; Williams, Duncan P.

doi:10.1016/j.apacoust.2019.107122

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…It is apparent that a PDF of the average ratio of the sound pressure amplitudes �� is not generally affected by smaller uncertainties or effective flow resistivity � �. This mirrors the behaviours seen in a similar study into excess attenuation [7]. The range ( ) and frequency ( ) shape the spectrum and PDF behaviours, with the range ( ) being the dominant parameter which effect becomes more pronounced as the uncertainty increases.…”

Section: Statistically (See Tablesupporting

confidence: 80%

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Pressure ratio and phase difference in a two-microphone system under uncertain outdoor sound propagation conditions

2020

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Predictions of outdoor sound propagation in uncertain conditions is a challenging task. Evidence suggest that using more than one receiver can reduce this effect of uncertainties. This paper studies via numerical simulations the effects of uncertainty in the source/receiver geometry and impedance ground condition on the sound pressure ratio recorded using the two-microphone method. A Monte Carlo method is employed study the effect of uncertainties in the range and ground parameters. The range and frequency are found to be key parameters which control the resultant probability density function for the absolute sound pressure ratio and phase difference. The introduction of small uncertainty only matters if the uncertainty is present in the distance between the source and receiver. Uncertainties in the impedance ground are found to have a negligible effect. The sound pressure ratio is affected by the uncertainty more strongly at a shorter range. These findings pave the way to the development of more robust methods for outdoor acoustic source localisation and identification from two-microphone data.

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Section: Statistically (See Tablesupporting

confidence: 80%

“…[4]). The final assumption of the uniform ground is acceptable because prior works have shown that a relatively large variability in the ground does not significantly affect the interaction paths over the scales adopted in this study [7].…”

Section: The Modelmentioning

confidence: 99%

Pressure ratio and phase difference in a two-microphone system under uncertain outdoor sound propagation conditions

2020

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“…Even the most uncertain case (source height (ℎ ) and impedance ground ( ) being uncertain) is now no longer a visible different distribution, unlike when the true range was less ( * = 250 ) (bottom row in Figure 5). A recent study by the authors highlighted how the impedance in particular uncertain geometries and statistical behaviour can affect inferences using certain methods [22]. This is likely why the simulations with multiple uncertainties are more strongly affected in the MAP methods at the shorter range ( * = 250 ), yet the increase in range reduces this effect.…”

Section: Visualisation Of Errorsmentioning

confidence: 99%

Likelihood maximisation techniques for ranging gunfire over grassland

2020

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A study into acoustic parameter inversion in the presence of a non-moving, homogeneous atmosphere and grassland impedance ground is carried out using methods of likelihood maximisation. Measured frequency-dependent sound pressure level and power spectra for a blank firing pistol are used to generate simulated data with added Gaussian error to represent variations usually present in real life experiments. Inference is carried out using maximum likelihood estimation (MLE) and maximum a priori (MAP) where model parameters are either given as known or restricted to some uncertain distribution bounded by realistic conditions. The quality of inference is assessed visually and statistically as the error between the true and inferred predictions for a given propagation range. Application of a prior (MAP) greatly improves inference accuracy compared to the sole maximisation of the likelihood function (MLE). It is shown that the use of a single octave band frequency window does not improve the quality of inference, whereas combinations of several low frequency octave bands do. Exact quantification of the true values of the ground and source height are seemingly less important as range increases beyond 500 . Although the techniques presented in this paper are for military/security applications, they are readily applicable to other acoustical problems, e.g. source characterisation in engineering noise control. The methods adopted are likely to benefit from higher-dimensional models, i.e. inhomogeneous atmospheres, complex terrain or urban environments.

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“…Model uncertainty includes physical and mathematical assumptions and numerical approximations [3], which are different for different models. Recent progress has been made to quantify parameter uncertainty such as uncertain geometries [10], atmospheric turbulence [11], meteorological states and ground properties [6,5,12]. In particular, by modelling the distribution of input parameters such as the atmospheric vertical wind speed profile, flow resistivity and porosity of the ground, derived from experimental data, Van Renterghem and Botteldooren [6] found that 95% confidence interval around predicted noise levels can be up to 10 dBA, even for short range propagation (< 250 m).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, by modelling the distribution of input parameters such as the atmospheric vertical wind speed profile, flow resistivity and porosity of the ground, derived from experimental data, Van Renterghem and Botteldooren [6] found that 95% confidence interval around predicted noise levels can be up to 10 dBA, even for short range propagation (< 250 m). Through investigating the uncertainty associated with source and receiver positions at short-range (< 200 m), Parry et al [10] found that the statistical distributions of propagation attenuation spectra were strongly affected by location uncertainties associated with these positions. The sensitivity of the parabolic equation model (PE), a widely-used model for outdoor noise propagation, has also been extensively investigated [13,14,9].…”

Section: Introductionmentioning

confidence: 99%

Multi-input model uncertainty analysis for long-range wind farm noise predictions

Nguyen¹,

Hansen²,

Zajamšek³

et al. 2022

Preprint

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One of the major sources of uncertainty in predictions of wind farm noise (WFN) reflect parametric and model structure uncertainty. The model structure uncertainty is a systematic uncertainty, which relates to uncertainty about the appropriate mathematical structure of the models. Here we quantified the model structure uncertainty in predicting WFN arising from multi-input models, including nine ground impedance and four wind speed profile models. We used a numerical ray tracing sound propagation model for predicting the noise level at different receivers. We found that variations between different ground impedance models and wind speed profile models were significant sources of uncertainty, and that these sources contributed to predicted noise level differences in excess of 10 dBA at distances greater than 3.5 km. We also found that differences between atmospheric vertical wind speed profile models were the main source of uncertainty in predicting WFN at long-range distances. When predicting WFN, it is important to

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Investigating uncertain geometries effect on sound propagation in a homogeneous and non-moving atmosphere over an impedance ground

Cited by 7 publications

References 7 publications

Pressure ratio and phase difference in a two-microphone system under uncertain outdoor sound propagation conditions

Pressure ratio and phase difference in a two-microphone system under uncertain outdoor sound propagation conditions

Likelihood maximisation techniques for ranging gunfire over grassland

Multi-input model uncertainty analysis for long-range wind farm noise predictions

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