Outside of shoebox rooms, acoustic diffraction phenomena are present and can influence important aspects of auditory perception, such as localisation. A simple extension of a shoebox room is an L-shaped room as it introduces a single diffracting edge. This paper presents two experiments carried out in L-shaped rooms in virtual reality. The first investigated whether the inclusion of diffraction modelling influences the perceived plausibility of the acoustic simulation, and the second to what extent newly developed efficient IIR filter diffraction models are equally plausible to the physically accurate Biot-Tolstoy-Medwin-Svensson (BTMS) model. The study compared diffraction of only the direct sound and diffraction of both direct and reflected sound. The results show that the inclusion of diffraction increased the perceived plausibility of the acoustic simulation. A statistically significant increase in plausibility was found by the addition of diffracted reflection paths, but only in the so-called shadow zone. The second experiment determined that the IIR filter diffraction models were similarly plausible to BTMS in 14 of 18 cases with a threshold of 0.5 on a 6-point Likert scale.

Abstract Precise elevation perception in binaural audio remains a challenge, despite extensive research on head-related transfer functions (HRTFs) and spectral cues. While prior studies have advanced our understanding of sound localization cues, the interplay between spectral features and elevation perception is still not fully understood. This paper presents a comprehensive analysis of over 600 subjects from 11 diverse public HRTF datasets, employing a convolutional neural network (CNN) model combined with explainable artificial intelligence (XAI) techniques to investigate elevation cues. In addition to testing various HRTF pre-processing methods, we focus on both within-dataset and inter-dataset generalization and explainability, assessing the model’s robustness across different HRTF variations stemming from subjects and measurement setups. By leveraging class activation mapping (CAM) saliency maps, we identify key frequency bands that may contribute to elevation perception, providing deeper insights into the spectral features that drive elevation-specific classification. This study offers new perspectives on HRTF modeling and elevation perception by analyzing diverse datasets and pre-processing techniques, expanding our understanding of these cues across a wide range of conditions.