arXiv — Machine Learning · August 24, 2026 · 3 min read

AudioWorldSim: Realistic Binaural Audio Datasets For World Models

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Computer Science > Sound

arXiv:2608.21075 (cs)

[Submitted on 21 Aug 2026]

Title:AudioWorldSim: Realistic Binaural Audio Datasets For World Models

Authors:Luis Vitor Zerkowski, Luiz Velho

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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 this https URL to facilitate reproducibility.

Comments:	7 pages, 3 figures
Subjects:	Sound (cs.SD); Machine Learning (cs.LG)
ACM classes:	I.m
Cite as:	arXiv:2608.21075 [cs.SD]
	(or arXiv:2608.21075v1 [cs.SD] for this version)
	https://doi.org/10.48550/arXiv.2608.21075 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Luis Vitor Zerkowski [view email]
[v1] Fri, 21 Aug 2026 13:17:06 UTC (1,023 KB)

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