mlsanityData to 7 Oct 2026

Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality

arXiv 2402.09245 ↗First posted 14 Feb 2024eess.AS cs.LG eess.SPcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 14 Feb 2024.

Builds on

3 of 4 references.

References · 3

  1. 2021Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AIcited by 579
  2. 2022SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learningcited by 93
  3. 2022L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environmentcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.