  The L3DAS21 Challenge is aimed at encouraging and fostering collaborative
research on machine learning for 3D audio signal processing, with particular
focus on 3D speech enhancement (SE) and 3D sound localization and detection
(SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65
hours 3D audio corpus, accompanied with a Python API that facilitates the data
usage and results submission stage. Usually, machine learning approaches to 3D
audio tasks are based on single-perspective Ambisonics recordings or on arrays
of single-capsule microphones. We propose, instead, a novel multichannel audio
configuration based multiple-source and multiple-perspective Ambisonics
recordings, performed with an array of two first-order Ambisonics microphones.
To the best of our knowledge, it is the first time that a dual-mic Ambisonics
configuration is used for these tasks. We provide baseline models and results
for both tasks, obtained with state-of-the-art architectures: FaSNet for SE and
SELDNet for SELD. This report is aimed at providing all needed information to
participate in the L3DAS21 Challenge, illustrating the details of the L3DAS21
dataset, the challenge tasks and the baseline models.
