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This study discusses acoustic dissipation, which contributes to inaccuraciesin impedance tube measurements. To improve the accuracy of these measurements,this paper introduces a transfer function model that integrates diversedissipation prediction models. Bayesian inference is used to estimate theimportant parameters included in these models, describing dissipationoriginating from various mechanisms, sound speed, and microphone positions. Byusing experimental measurements and considering a hypothetical air layer infront of a rigid termination as the material under test, Bayesian parameterestimation allows a substantial enhancement in characterization accuracy byincorporating the dissipation and sound speed estimates. This approacheffectively minimizes residual absorption coefficients attributed to bothboundary-layer effects and air medium relaxation. Incorporation of dissipationmodels leads to a substantial reduction (to 1\%) in residual absorptioncoefficients. Moreover, the use of accurately estimated parameters furtherenhances the accuracy of actual tube measurements of materials using thetwo-microphone transfer function method.

Bayesian estimation of dissipation and sound speed in tube measurements using a transfer-function model