Rules and requirements

The goal of the challenge is to foster research on machinelearning for 3D audio. All participants should adhere to thefollowing rules to be eligible for the challenge:

• All participants must submit the obtained results for at least one of the 2 tasks. The results should be accompanied by a paper describing the proposed method.
• Each individual participant cannot be included in multiple participating teams. Therefore, a participant is allowed to submit only one set of results.
  
• Winners will be selected according to the best performance for each single task, separately. Therefore, one winner for each task will be selected.
  
• There are no restrictions on the proposed methodologies. However, in case of a tie, the Challenge Committee will take into account the novelty and originality of the proposed approach. Also, a method that can be used for both the 1-mic and 2-mic configurations will be positively evaluated.
  
• Participants are not restricted to use the L3DAS22 dataset only. It is in fact allowed to augment this dataset and/or to integrate additional data to train/pre-train the models.
  
• The Challenge can have up to 5 papers from the top ranked teams. The format should be consistent with ICASSP regular paper, and should be submitted before the camera-ready deadline.
  
• Accepted papers will be presented at a Signal Processing Grand Challenge special session of the IEEE ICASSP 2022 conference. Authors, who are not interested in participating the challenge but want to make a contribution to the topic, are encouraged to submit a paper to this track, even without specifically use the proposed datasets.