AudioWorldSim: Realistic Binaural Audio Datasets For World Models
Luis Vitor Zerkowski, Luiz Velho
cs.SD cs.LG
Abstract
This technical report presents AudioWorldSim, an open-source platform designed to generate realistic binaural audio datasets and advance research in audio-based machine learning, particularly world models. Built as a custom extension of Meta's SoundSpaces 2.0 platform, AudioWorldSim leverages their comprehensive acoustics framework, but focuses on the automatic rollout of random agent navigations, as well as implements crucial fixes to how continuous sound is composed. AudioWorldSim is made publicly available to the research community at https://github.com/Luizerko/AudioWorldSim to facilitate reproducibility.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.