# Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality

arXiv: https://arxiv.org/abs/2402.09245 · HTML: https://mlsanity.com/paper/2402.09245/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning](https://mlsanity.com/paper/2206.08312/) (2022)
- [L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment](https://mlsanity.com/paper/2202.10372/) (2022)

## Built on by (most cited first, 4 of 4)

- [Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning](https://mlsanity.com/paper/2505.07365/) (2025)
- [MB-RIRs: a Synthetic Room Impulse Response Dataset with Frequency-Dependent Absorption Coefficients](https://mlsanity.com/paper/2507.09750/) (2025)
- [Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception](https://mlsanity.com/paper/2601.09413/) (2026)
- [Generative Models for Helmholtz Equation Solutions: A Dataset of Acoustic Materials](https://mlsanity.com/paper/2510.09657/) (2025)

## Abstract (arXiv)

The primary goal of the L3DAS23 Signal Processing Grand Challenge at ICASSP 2023 is to promote and support collaborative research on machine learning for 3D audio signal processing, with a specific emphasis on 3D speech enhancement and 3D Sound Event Localization and Detection in Extended Reality applications. As part of our latest competition, we provide a brand-new dataset, which maintains the same general characteristics of the L3DAS21 and L3DAS22 datasets, but with first-order Ambisonics recordings from multiple reverberant simulated environments. Moreover, we start exploring an audio-visual scenario by providing images of these environments, as perceived by the different microphone positions and orientations. We also propose updated baseline models for both tasks that can now support audio-image couples as input and a supporting API to replicate our results. Finally, we present the results of the participants. Further details about the challenge are available at https://www.l3das.com/icassp2023.