METAMAT 01: A semi-analytic Solution for Benchmarking Wave Propagation Simulations of homogeneous Absorbers in 1D/3D and 2D

The development of acoustic simulation workflows in the time-domain description is essential for predicting the sound of aeroacoustic or other transient acoustic effects. A common practice for noise mitigation is using absorbers. The modeling of these acoustic absorbers is typically provided in the frequency domain. Several, methods established bridging this gap, investigating methods to model absorber in the time domain. Therefore, this short article, describes the analytic solution in time-domain for benchmarking absorber simulations with infinite 1D, 2D, and 3D domains. Connected to the analytic solution, a Matlab script is provided to easily obtain the reference solution. The reference codes are provided as benchmark solution in the EAA TCCA Benchmarking database as METAMAT 01.

First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria

This study investigates the application of single and two-stage 2D-object detection algorithms like You Only Look Once (YOLO), Real-Time DEtection TRansformer (RT-DETR) algorithm for automated object detection to enhance road safety for autonomous driving on Austrian roads. The YOLO algorithm is a state-of-the-art real-time object detection system known for its efficiency and accuracy. In the context of driving, its potential to rapidly identify and track objects is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. The research focuses on the unique challenges posed by the road conditions and traffic scenarios in Austria. The country's diverse landscape, varying weather conditions, and specific traffic regulations necessitate a tailored approach for reliable object detection. The study utilizes a selective dataset comprising images and videos captured on Austrian roads, encompassing urban, rural, and alpine environments.

Implementation of an aeroacoustic simulation pipeline using openCFS-Acoustics and openCFS-Data applied to human phonation

The human phonation process be modeled using the Finite Element Method (FEM) which provides a detailed representation of the voice production process. A software implementation in C++ using FEM (openCFS) has been used to simulate the phonation process. The FEM model consists of a 3D mesh of the upper human airways. The simVoice model provides an accurate representation of the phonation process and was valid in several publications. In this article, we show how to set up the model using openCFS and openCFS-Data.

Machine-learning applied to classify flow-induced sound parameters from simulated human voice

Disorders of voice production have severe effects on the quality of life of the affected individuals. A simulation approach is used to investigate the cause-effect chain in voice production showing typical characteristics of voice such as sub-glottal pressure and of functional voice disorders as glottal closure insufficiency and left-right asymmetry. Therewith, 24 different voice configurations are simulated in a parameter study using a previously published hybrid aeroacoustic simulation model. Based on these 24 simulation configurations, selected acoustic parameters (HNR, CPP, ...) at simulation evaluation points are correlated with these simulation configuration details to derive characteristic insight in the flow-induced sound generation of human phonation based on simulation results. Recently, several institutions studied experimental data, of flow and acoustic properties and correlated it with healthy and disordered voice signals. Upon this, the study is a next step towards a detailed dataset definition, the dataset is small, but the definition of relevant characteristics are precise based on the existing simulation methodology of simVoice. The small datasets are studied by correlation analysis, and a Support Vector Machine classifier with RBF kernel is used to classify the representations. With the use of Linear Discriminant Analysis the dimensions of the individual studies are visualized. This allows to draw correlations and determine the most important features evaluated from the acoustic signals in front of the mouth. The GC type can be best discriminated based on CPP and boxplot visualizations. Furthermore and using the LDA-dimensionality-reduced feature space, one can best classify subglottal pressure with 91.7\% accuracy, independent of healthy or disordered voice simulation parameters.