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Individual Head-Related Transfer Functions (HRTFs) are necessary for the accurate rendering of virtual scenes. However, their acquisition is challenging given the complex pinna shape. Numerical methods can be leveraged to compute HRTFs on meshes originating from precise scans of a subject. Although photogrammetry can be used for the scanning, its inaccuracy might affect the spatial cues of simulated HRTFs. This paper aims to assess the significance of the photogrammetric error affecting a Neumann KU100 dummy head scan. The geometrical differences between the photogrammetric scan and a laser scan are mainly located at the pinna cavities. The computed photogrammetric HRTFs, compared to measured and simulated data using objective and perceptually inspired metrics, show deviation in high frequency spectral features, stemming from the photogrammetric scanning error. This spectral deviation hinders the modelled elevation perception with photogrammetric HRTFs to levels comparable to renderings with nonindividual data. Extracting the photogrammetric geometry at individual ear cavities and merging it to the laser mesh, an assessment of the influence of the inaccuracy at different pinna structures is conducted. Correlation analysis between acoustic and geometrical metrics computed on the results is used to identify the most relevant geometrical metrics in relation to the HRTFs.

The bending wave stop band behavior of locally resonant metamaterial and phononic crystal plates can strongly improve the vibration performance of flexible plates in targeted frequency ranges. Their acoustic insulation performance, however, is found to strongly depend on the stop band mechanism, also influencing the frequency range of improvement. While sound transmission loss improvements are predicted for both infinite periodic plates, the finite phononic crystal plate performance can differ significantly.

Recently, it has been shown that a Time Reversal MUltiple Signal Classification (TR-MUSIC) algorithm can be employed to detect defects in samples, which are both challenging in terms of material and in terms of geometrical complexity. This can be achieved by lowering the detection frequency as compared to most other TR and TR-MUSIC applications. In this case, the method operates in a low to mid-frequency range, where accurate models are still realizable. The method relies on the measurement of the frequency response function (FRF) between multiple excitation and sensor locations. These are gathered in a matrix, which then is decomposed in its singular vectors, serving as input for a MUSIC algorithm. In order to improve the applicability of this method, it is shown how to adapt the algorithm, in order to reduce the number of excitation locations or the number of sensors significantly. This results in a considerable speed-up for the application of this non-destructive testing strategy. Furthermore, it is investigated numerically how the robustness of the detection result under the influence of random measurement errors behaves if the number of excitation/sensor locations is reduced. Additionally, the dependency between, the decrease of the number of excitation/sensor locations and the types of defects, which can be detected is studied.