Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality

Author:

Christian Marinoni, Riccardo Fosco Gramaccioni, Changan Chen, Aurelio Uncini, Danilo Comminiello

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Machine Learning (cs.LG), Signal Processing (eess.SP)

journal:

date:

2024-02-14 00:00:00

Abstract

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.

PDF: Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality.pdf