Acoustic scene classification (ASC) is a crucial research problem in
computational auditory scene analysis, and it aims to recognize the unique
acoustic characteristics of an environment. One of the challenges of the ASC
task is domain shift caused by a distribution gap between training and testing
data. Since 2018, ASC challenges have focused on the generalization of ASC
models across different recording devices. Although this task in recent years
has achieved substantial progress in device generalization, the challenge of
domain shift between different regions, involving characteristics such as time,
space, culture, and language, remains insufficiently explored at present. In
addition, considering the abundance of unlabeled acoustic scene data in the
real world, it is important to study the possible ways to utilize these
unlabelled data. Therefore, we introduce the task Semi-supervised Acoustic
Scene Classification under Domain Shift in the ICME 2024 Grand Challenge. We
encourage participants to innovate with semi-supervised learning techniques,
aiming to develop more robust ASC models under domain shift.

在 ICME 2024 大挑战中，我们引入了半监督领域迁移音景分类任务，鼓励参与者创新半监督学习技术，旨在开发更具鲁棒性的 ASC 模型。