A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets

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.

Automatic Counting and Classification of Mosquito Eggs in Field Traps

The analysis of the field traps where the mosquitoes insert their eggs is vital to check that the sterile insect technique (SIT) is working properly. This is because the number of hatched eggs may indicate that the sterile males are not competing with the wild ones. Nowadays, the study of the traps is done manually by microscope and is very time-consuming and prone to human error. This paper presents an automatic trap survey. For this purpose, a device has been designed that automatically scans the slat obtaining different overlapping photos. Subsequently, the images are analyzed by a Mask-RCNN neural network that segments the eggs and classifies them into 2 classes: full or hatch