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Individual Head-Related Transfer Functions (HRTFs), crucial for realistic virtual audio rendering, can be efficiently numerically computed from precise three-dimensional head and ear scans. While photogrammetry scanning is promising, it generally lacks accuracy, leading to HRTFs showing significant perceptual deviation from reference data, mainly due to scanning errors affecting the most occluded pinna structures. This paper examines the application of Deep Neural Networks (DNNs) for denoising photogrammetric ear scans. Several DNNs, fine-tuned on pinna samples corrupted with synthetic error modelled to mimic that observed in photogrammetric dummy head scans, are tested and benchmarked against a classical denoising method. One DNN is further modified and retrained to enhance its denoising performance. The comparison of HRTFs derived from original and denoised scans against reference data shows that the best-performing DNN marginally reduces the deviation of photogrammetric dummy head HRTFs to levels closer to accurately measured ones. Additionally, correlation analysis between geometric and HRTF metrics, computed on the scanned point clouds and their corresponding HRTFs, is used to identify key measures for evaluating the deviation between target and reference scans. These findings are expected to guide the selection of relevant loss functions and foster improvements in this and similar DNN models.

Dispersion diagrams play a crucial role in examining, analyzing and designing wave propagation in periodic structures. Despite their ubiquity and current research interest, introductory papers and reference scripting tailored to novel researchers in the field are lacking. This paper aims to address this gap, by presenting a comprehensive educational resource for researchers starting in the field of periodic structures and more specifically on the study of dispersion relations captured by dispersion surfaces or curves in dispersion diagrams. The objective is twofold. A first objective is to give a detailed explanation of dispersion diagrams, with graphical illustrations. Secondly, a documented Matlab code is provided to compute dispersion curves of 3D structures with 2D periodicity using the so-called inverse approach. These dispersion curves are obtained with numerical simulations using the finite element method. The code is written for elastic wave propagation and orthogonal periodicity directions, but can be extended to other types of linear wave propagation, non-orthogonal periodicity directions or 1D and 3D periodicity. The aim of this code is to serve as a starting point for novice researchers in the field, to facilitate their understanding of different aspects of dispersion diagrams and serve as a stepping stone in their future research., Comment: 43 pages, 23 figures

The analysis of sound and vibrations is often performed in the frequency domain, implying the assumption of steady-state behaviour and time-harmonic excitation. External excitations, however, may be transient rather than time-harmonic, requiring time-domain analysis. Some material properties, e.g.\ often used to represent for damping treatments, are still described in the frequency domain, which complicates simulation in time. In this paper, we present a method for the linearization of finite element models with nonlinear frequency dependencies. The linearization relies on the rational approximation of the finite element matrices by the AAA method. We introduce the Extended AAA method, which is classical AAA combined with a degree two polynomial term to capture the second order behaviour of the models. A filtering step is added for removing unstable poles.