Implementing Continuous HRTF Measurement in Near-Field

Head-related transfer function (HRTF) is an essential component to create an immersive listening experience over headphones for virtual reality (VR) and augmented reality (AR) applications. Metaverse combines VR and AR to create immersive digital experiences, and users are very likely to interact with virtual objects in the near-field (NF). The HRTFs of such objects are highly individualized and dependent on directions and distances. Hence, a significant number of HRTF measurements at different distances in the NF would be needed. Using conventional static stop-and-go HRTF measurement methods to acquire these measurements would be time-consuming and tedious for human listeners. In this paper, we propose a continuous measurement system targeted for the NF, and efficiently capturing HRTFs in the horizontal plane within 45 secs. Comparative experiments are performed on head and torso similar (HATS) and human listeners to evaluate system consistency and robustness.

A Strongly-Labelled Polyphonic Dataset of Urban Sounds with Spatiotemporal Context

This paper introduces SINGA:PURA, a strongly labelled polyphonic urban sound dataset with spatiotemporal context. The data were collected via several recording units deployed across Singapore as a part of a wireless acoustic sensor network. These recordings were made as part of a project to identify and mitigate noise sources in Singapore, but also possess a wider applicability to sound event detection, classification, and localization. This paper introduces an accompanying hierarchical label taxonomy, which has been designed to be compatible with other existing datasets for urban sound tagging while also able to capture sound events unique to the Singaporean context. This paper details the data collection, annotation, and processing methodologies for the creation of the dataset. We further perform exploratory data analysis and include the performance of a baseline model on the dataset as a benchmark.