# AudioWorldSim: Realistic Binaural Audio Datasets For World Models

arXiv: https://arxiv.org/abs/2608.21075 · HTML: https://mlsanity.com/paper/2608.21075/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation](https://mlsanity.com/paper/2007.04954/) (2020)
- [The Audio-Visual BatVision Dataset for Research on Sight and Sound](https://mlsanity.com/paper/2303.07257/) (2023)

## Built on by (most cited first, 2 of 2)

- [BinauralVAE: Spatial Audio Reconstruction For World Models](https://mlsanity.com/paper/2609.06837/) (2026)
- [SAIL: Spatial Audio Intelligence with Large Language Models via Disentangled Acoustic-Spatial Encoding and Dual-Stream Q-Former](https://mlsanity.com/paper/2609.34347/) (2026)

## Abstract (arXiv)

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.